Artificial intelligence in the detection and classification of dental caries.

STATEMENT OF PROBLEM: Automated detection of dental caries could enhance early detection, save clinician time, and enrich treatment decisions. However, a reliable system is lacking. PURPOSE: The purpose of this study was to train a deep learning model and to assess its ability to detect and classify dental caries. MATERIAL AND METHODS: Bitewings radiographs with a 1876×1402-pixel resolution were collected, segmented, and anonymized with a radiographic image analysis software program and were identified and classified according to the modified King Abdulaziz University (KAU) classification for dental caries. The method was based on supervised learning algorithms trained on semantic segmentation tasks. RESULTS: The mean score for the intersection-over-union of the model was 0.55 for proximal carious lesions on a 5-category segmentation assignment and a mean F1 score of 0.535 using 554 training samples. CONCLUSIONS: The study validated the high potential for developing an accurate caries detection model that will expedite caries identification, assess clinician decision-making, and improve the quality of patient care.

Hybrid Clustering and Routing Algorithm with Threshold-Based Data Collection for Heterogeneous Wireless Sensor Networks.

The concept of the internet of things (IoT) motivates us to connect bulk isolated heterogeneous devices to automate report generation without human interaction. Energy-efficient routing algorithms help to prolong the network lifetime of these energy-restricted smart devices that are connected by means of wireless sensor networks (WSNs). Current vendor-level advancements enable algorithm-level flexibility to design protocols to concurrently collect multiple application data while enforcing the reduction of energy expenditure to gain commercial success in the industrial stage. In this paper, we propose a hybrid clustering and routing algorithm with threshold-based data collection for heterogeneous wireless sensor networks. In our proposed model, homogeneous and heterogeneous nodes are deployed within specific regions. To reduce unnecessary data transmission, threshold-based conditions are presented to prevent unnecessary transmission when minor or no change is observed in the simulated and real-world applications. We further extend our proposed multi-hop model to achieve more network stability in dense and larger network areas. Our proposed model shows enhancement in terms of load balancing and end-to-end delay as compared to the other threshold-based energy-efficient routing protocols, such as the threshold-sensitive stable election protocol (TSEP), threshold distributed energy-efficient clustering (TDEEC), low-energy adaptive clustering hierarchy (LEACH), and energy-efficient sensor network (TEEN).

Theoretical Analysis on Absorption of Carbon Dioxide (CO2) into Solutions of Phenyl Glycidyl Ether (PGE) Using Nonlinear Autoregressive Exogenous Neural Networks.

In this paper, we analyzed the mass transfer model with chemical reactions during the absorption of carbon dioxide (CO2) into phenyl glycidyl ether (PGE) solution. The mathematical model of the phenomenon is governed by a coupled nonlinear differential equation that corresponds to the reaction kinetics and diffusion. The system of differential equations is subjected to Dirichlet boundary conditions and a mixed set of Neumann and Dirichlet boundary conditions. Further, to calculate the concentration of CO2, PGE, and the flux in terms of reaction rate constants, we adopt the supervised learning strategy of a nonlinear autoregressive exogenous (NARX) neural network model with two activation functions (Log-sigmoid and Hyperbolic tangent). The reference data set for the possible outcomes of different scenarios based on variations in normalized parameters (α1, α2, ß1, ß2, k) are obtained using the MATLAB solver "pdex4". The dataset is further interpreted by the Levenberg-Marquardt (LM) backpropagation algorithm for validation, testing, and training. The results obtained by the NARX-LM algorithm are compared with the Adomian decomposition method and residual method. The rapid convergence of solutions, smooth implementation, computational complexity, absolute errors, and statistics of the mean square error further validate the design scheme's worth and efficiency.

Treatment of non-united femoral neck fracture by a novel subtrochanteric angulation lateral translation valgus osteotomy (SALVA osteotomy).

PURPOSE: The ideal treatment of non-united and neglected fracture neck femur in the young adult still remains unclear and is characterized by many biological and biomechanical challenges. METHODS: Twenty-one patients with non-united or neglected fracture neck femur aged between 19 and 50 years were treated by a novel subtrochanteric valgus osteotomy and were followed up for a mean of 26.7 months. Patients were assessed by radiological parameters, the Harris Hip Score, Oxford Knee Score, and Askin Bryan Criteria to categorize the overall outcome of the patients at 24 months. Other outcome measures included the occurrence of AVN, adductor lever arm, leg length discrepancy, and mechanical implant failure. RESULTS: All patients treated with the SALVA osteotomy consolidated and displayed a marked improvement of functional and radiological outcome measures. Nevertheless, there were 2 mechanical failures in patients with marked osteopenia and three developed AVN. CONCLUSIONS: In patients with un-united/neglected fracture neck femur, SALVA osteotomy appears to be reliable and reproducible. It also restores the abductor lever arm and improves the leg length discrepancy. Technically less demanding conversion to arthroplasty remains still possible prospectively.

Improving access to emergent spinal care through knowledge translation: an ethnographic study.

BACKGROUND: For patients and family members, access to timely specialty medical care for emergent spinal conditions is a significant stressor to an already serious condition. Timing to surgical care for emergent spinal conditions such as spinal trauma is an important predictor of outcome. However, few studies have explored ethnographically the views of surgeons and other key stakeholders on issues related to patient access and care for emergent spine conditions. The primary study objective was to determine the challenges to the provision of timely care as well as to identify areas of opportunities to enhance care delivery. METHODS: An ethnographic study of key administrative and clinical care providers involved in the triage and care of patients referred through CritiCall Ontario was undertaken utilizing standard methods of qualitative inquiry. This comprised 21 interviews with people involved in varying capacities with the provision of emergent spinal care, as well as qualitative observations on an orthopaedic/neurosurgical ward, in operating theatres, and at CritiCall Ontario's call centre. RESULTS: Several themes were identified and organized into categories that range from inter-professional collaboration through to issues of hospital-level resources and the role of relationships between hospitals and external organizations at the provincial level. Underlying many of these issues is the nature of the medically complex emergent spine patient and the scientific evidentiary base upon which best practice care is delivered. Through the implementation of knowledge translation strategies facilitated from this research, a reduction of patient transfers out of province was observed in the one-year period following program implementation. CONCLUSIONS: Our findings suggest that competing priorities at both the hospital and provincial level create challenges in the delivery of spinal care. Key stakeholders recognized spinal care as aligning with multiple priorities such as emergent/critical care, medical through surgical, acute through rehabilitative, disease-based (i.e. trauma, cancer), and wait times initiatives. However, despite newly implemented strategies, there continues to be increasing trends over time in the number of spinal CritiCall Ontario referrals. This reinforces the need for ongoing inter-professional efforts in care delivery that take into account the institutional contexts that may constrain individual or team efforts.

Evaluation of Tamsulosin 0.4 mg versus 0.8 mg in management of lower urinary tract symptoms due to benign prostatic enlargement.

PURPOSE: To compare the efficacy and the safety of Tamsulosin 0.4 mg/day and 0.8 mg/day in patients suffering from lower urinary tract symptoms due to benign prostatic obstruction. PATIENTS AND METHODS: A prospective interventional, double-blinded, controlled study was carried out on 93 patients who met the criteria and divided randomly into two groups: group A for Tamsulosin 0.4 mg/day and group B for Tamsulosin 0.8 mg/day. International prostate symptom score, post void residual urine volume, and maximum flow rate of urine were assessed before and after 4 weeks of treatment. RESULTS: Both study groups showed a significant reduction in storage sub-score but only frequency was significantly reduced in group B (P < 0.001). On the other hand, Tamsulosin 0.8 mg was superior to Tamsulosin 0.4 mg regarding voiding sub-score except for straining (P = 0.325). Accordingly, the total international prostate symptom score was significantly improved in group B versus group A (P < 0.001). Furthermore, maximum flow rate and post-void residual urine volume were notably improved in Group B as compared to Group A (P < 0.001). Of all adverse events only dizziness was noted to be statistically significant in Group B versus Group A (P < 0.001). CONCLUSION: Tamsulosin 0.8 mg has shown better outcomes in treating patients who suffer from lower urinary tract symptoms due to benign prostatic enlargement than Tamsulosin 0.4 mg, and besides that, it is well tolerated. TRIAL REGISTRATION NUMBER: M S 292/2020, SID: 373, date: 22/4/2020.

iAFPs-Mv-BiTCN: Predicting antifungal peptides using self-attention transformer embedding and transform evolutionary based multi-view features with bidirectional temporal convolutional networks.

Globally, fungal infections have become a major health concern in humans. Fungal diseases generally occur due to the invading fungus appearing on a specific portion of the body and becoming hard for the human immune system to resist. The recent emergence of COVID-19 has intensely increased different nosocomial fungal infections. The existing wet-laboratory-based medications are expensive, time-consuming, and may have adverse side effects on normal cells. In the last decade, peptide therapeutics have gained significant attention due to their high specificity in targeting affected cells without affecting healthy cells. Motivated by the significance of peptide-based therapies, we developed a highly discriminative prediction scheme called iAFPs-Mv-BiTCN to predict antifungal peptides correctly. The training peptides are encoded using word embedding methods such as skip-gram and attention mechanism-based bidirectional encoder representation using transformer. Additionally, transform-based evolutionary features are generated using the Pseduo position-specific scoring matrix using discrete wavelet transform (PsePSSM-DWT). The fused vector of word embedding and evolutionary descriptors is formed to compensate for the limitations of single encoding methods. A Shapley Additive exPlanations (SHAP) based global interpolation approach is applied to reduce training costs by choosing the optimal feature set. The selected feature set is trained using a bi-directional temporal convolutional network (BiTCN). The proposed iAFPs-Mv-BiTCN model achieved a predictive accuracy of 98.15 % and an AUC of 0.99 using training samples. In the case of the independent samples, our model obtained an accuracy of 94.11 % and an AUC of 0.98. Our iAFPs-Mv-BiTCN model outperformed existing models with a ~4 % and ~5 % higher accuracy using training and independent samples, respectively. The reliability and efficacy of the proposed iAFPs-Mv-BiTCN model make it a valuable tool for scientists and may perform a beneficial role in pharmaceutical design and research academia.

Compromise optimum allocation in neutrosophic multi-character survey under stratified random sampling using neutrosophic fuzzy programming.

Survey sampling has wide range of applications in social and scientific investigation to draw inference about the unknown parameter of interest. In complex surveys, the sample information about the study variable cannot be expressed by a precise number under uncertain environment due fuzziness and indeterminacy. Therefore, this information is expressed by neutrosophic numbers rather than the classical numbers. The neutrosophic statistics, which is generalization of classical statistics, deals with the neutrosophic data that has some degree of indeterminacy and fuzziness. In this study, we investigate the compromise optimum allocation problem for estimating the population means of the neutrosophic study variables in a multi-character stratified random sampling under uncertain per unit measurement cost. We proposed the intuitionistic fuzzy cost function, modeling the fuzzy uncertainty in stratum per unit measurement cost. The compromise optimum allocation problem is formulated as a multi-objective intuitionistic fuzzy optimization problem. The solution methodology is suggested using neutrosophic fuzzy programming and intuitionistic fuzzy programming approaches. A numerical study includes the means estimation of atmospheric variables is presented to explore the real-life application, explain the mathematical formulation, and efficiency comparison with some existing methods. The results show that the suggested methods produce more precise estimates with less utilization of survey resources as compared to some existing methods. The Python is used for statistical analysis, graphical designing and numerical optimization problems are solved using GAMS.

Recent Advances in Machine Learning-Based Models for Prediction of Antiviral Peptides.

Viruses have killed and infected millions of people across the world. It causes several chronic diseases like COVID-19, HIV, and hepatitis. To cope with such diseases and virus infections, antiviral peptides (AVPs) have been applied in the design of drugs. Keeping in view the significant role in pharmaceutical industry and other research fields, identification of AVPs is highly indispensable. In this connection, experimental and computational methods were proposed to identify AVPs. However, more accurate predictors for boosting AVPs identification are highly desirable. This work presents a thorough study and reports the available predictors of AVPs. We explained applied datasets, feature representation approaches, classification algorithms, and evaluation parameters of performance. In this study, the limitations of the existing studies and the best methods were emphasized. Provided the pros and cons of the applied classifiers. The future insights demonstrate efficient feature encoding approaches, best feature optimization schemes, and effective classification techniques that can improve the performance of novel method for accurate prediction of AVPs.

cACP-DeepGram: Classification of anticancer peptides via deep neural network and skip-gram-based word embedding model.

Cancer is a Toxic health concern worldwide, it happens when cellular modifications cause the irregular growth and division of human cells. Several traditional approaches such as therapies and wet laboratory-based methods have been applied to treat cancer cells. However, these methods are considered less effective due to their high cost and diverse side effects. According to recent advancements, peptide-based therapies have attracted the attention of scientists because of their high selectivity. Peptide therapy can efficiently treat the targeted cells, without affecting the normal cells. Due to the rapid increase of peptide sequences, an accurate prediction model has become a challenging task. Keeping the significance of anticancer peptides (ACPs) in cancer treatment, an intelligent and reliable prediction model is highly indispensable. In this paper, a FastText-based word embedding strategy has been employed to represent each peptide sample via a skip-gram model. After extracting the peptide embedding descriptors, the deep neural network (DNN) model was applied to accurately discriminate the ACPs. The optimized parameters of DNN achieved an accuracy of 96.94 %, 93.41 %, and 94.02 % using training, alternate, and independent samples, respectively. It was observed that our proposed cACP-DeepGram model outperformed and reported ~10 % highest prediction accuracy than existing predictors. It is suggested that the cACP-DeepGram model will be a reliable tool for scientists and might play a valuable role in academic research and drug discovery. The source code and the datasets are publicly available at https://github.com/shahidakbarcs/cACP-DeepGram.

NLP-BCH-Ens: NLP-based intelligent computational model for discrimination of malaria parasite.

Plasmodium falciparum causes malaria, which is an infectious and fatal disease. In early days, malaria-infected cells were diagnosed using a microscope. owing to a huge number of instances for analysis and intricacy of time, it may lead to false detection. Automated parasite detection technologies are in high demand due to increased time consumption and erroneous detection. To create effective cures and treatments, it is critical to use an accurate approach for predicting malaria parasite. Here, numerous protein sequences formulation techniques namely: discrete methods, Biochemical, physiochemical and Natural language processing techniques are applied for transformation of protein sequences in to numerical descriptors. Four classification algorithms are utilized and the anticipated results of these classifiers were then fused to establish ensemble classification model via simple majority and genetic algorithm. In addition, BCH error correction code is incorporated with support vector machine using all the feature spaces. The simulated results demonstrate the remarkable achievement of proposed compared to previous models. Thus, our proposed model may be an effective tool for discriminating the secretory and non-secretory proteins of malaria parasite.

Target-AMP: Computational prediction of antimicrobial peptides by coupling sequential information with evolutionary profile.

Antimicrobial peptides (AMPs) are gaining a lot of attention as cutting-edge treatments for many infectious disorders. The effectiveness of AMPs against bacteria, fungi, and viruses has persisted for a long period, making them the greatest option for addressing the growing problem of antibiotic resistance. Due to their wide-ranging actions, AMPs have become more prominent, particularly in therapeutic applications. The prediction of AMPs has become a difficult task for academics due to the explosive increase of AMPs documented in databases. Wet-lab investigations to find anti-microbial peptides are exceedingly costly, time-consuming, and even impossible for some species. Therefore, in order to choose the optimal AMPs candidate before to the in-vitro trials, an efficient computational method must be developed. In this study, an effort was made to develop a machine learning-based classification system that is effective, accurate, and can distinguish between anti-microbial peptides. The position-specific-scoring-matrix (PSSM), Pseudo Amino acid composition, di-peptide composition, and combination of these three were utilized in the suggested scheme to extract salient aspects from AMPs sequences. The classification techniques K-nearest neighbor (KNN), Random Forest (RF), and Support Vector Machine (SVM) were employed. On the independent dataset and training dataset, the accuracy levels achieved by the suggested predictor (Target-AMP) are 97.07% and 95.71%, respectively. The results show that, when compared to other techniques currently used in the literature, our Target-AMP had the best success rate.

Users' Rating Predictions Using Collaborating Filtering Based on Users and Items Similarity Measures.

The social media has made the world a global world and we, in addition to, as part of physical society, are now part of the virtual society as well. There has been the generation of a large amount of information over the social web. By way of increasing online information, new opportunities emerged, and diverse issues have been raised, which have attracted researchers to address these research problems. In this current age, where online business and e-commerce are part of our daily lives, recommender systems (RSs) are very effective for information filtering. RSs play a significant role in our lives by assisting users in recommending items and services what they may be interesting in to purchase or avail. In this research work, our goal is to predict the users' ratings for various items, which are an active research area in collaborative filtering (CF). In this work, we have explored various similarity measures based on user-user and item-item rating predictions on different datasets by applying collaborative filtering approaches. The comparison of item-item and user-user CF algorithms such as user K-Nearest Neighbour using cosine; similarity, Pearson correlation as well as item-based K-NN using these measures with baseline approaches and matrix-based methods such as Matrix factorization (MF), biased MF, and factor wise MF has been carried out. For empirical-based comparison analysis, diverse approaches have been selected such as slope one, random, and global average, and it revealed that item-item K-NN using Pearson correlation has outperformed all other applied approaches. For the experiments, three real world and widely used datasets of MovieLens 1M, CiaoDVD, and MovieLens 100k have been used. The empirical-based results have been evaluated by using standard performance evaluation measures of RMSE and MAE.

Repair of spondylolysis using a pedicle screw U-shaped rod construct: A preliminary study of 25 young patients with a mean follow-up of 24 months.

STUDY DESIGN: Prospective case series, therapeutic Level IV. OBJECTIVES: Functional and radiographic outcome evaluation of patients with spondylolysis treated with pars interarticularis defect repair with iliac bone grafting and application of a construct consisting of a pair of polyaxial pedicle screws connected by a U-shaped rod passing beneath the spinous process. METHODS: Twenty-five patients (27 operated lumbar levels) with an average of 20 months of follow-up (range 12-24 m) with spondylolysis who met our inclusion criteria were treated with the above-mentioned technique. Functional assessment was by the Visual Analog Score (VAS) for low back pain (LBP) and Oswestry Disability Index (ODI). Fusion was confirmed with plain x-rays and when indicated with computed tomography scan. Return to activities of daily living (ADL) was also assessed. RESULTS: There were 16 males (64%) and 9 females (36%), with a mean age of 18 ± 3 years at surgery, with a mean operating time of 79 ± 13 min and a mean blood loss of 186 ± 57 ml. ODI significantly improved from a mean of 63 ± 7 preoperatively to 10 ± 4 at 12 months postoperatively (P < 0.001). The mean preoperative LBP VAS score 8 ± 1 showed also a statistically significant decrease of values to 1 ± 1 at 12 months, (P < 0.001). At 12 m, all patients returned to unrestricted ADL. Pars healing was present in 19 patients (76%) at 6 months and in all patients at 12 months. CONCLUSIONS: Polyaxial pedicular screws with a U-shaped rod offer an effective and reproducible treatment for spondylolysis with an appropriate fusion rate, predictable return to daily activities, and good pain relief in young adults.

Insertion of double J stent as a therapeutic test in management of adults presenting with loin pain and equivocal ureteropelvic junction obstruction.

PURPOSE: To evaluate temporary double J ureteric stenting (TDJS) as a test to diagnose ureteropelvic junction obstruction (UPJO) in equivocal cases. MATERIALS AND METHODS: One hundred and nine consecutive adult patients with loin pain and ipsilateral equivocal UPJO (EqUPJO) on radioisotope diuretic renogram (RDR) were enrolled in the present study. All patients underwent TDJS for 3 weeks. According to the response (relief of pain) to TDJS, which was assessed by visual analogue pain score (VAPS), patient preference and further management, patients were classified into five groups: Group Ia non-responders who elected conservative treatment for their condition, Group Ib non-responders who elected intervention, Group IIa responders who elected conservative treatment and Group IIb responders who elected intervention. Last group patients were randomized to either early (Group IIb(1)) or delayed intervention 3 months later (Group IIb(2)). Intervention was in the form of pyeloplasty (24 patients) and endopyelotomy (38 patients). All patients were followed up by measuring VAPS and RDR. RESULTS: After at least 6 months from management decision, 97 patients were available for evaluation. The VAPS dropped by 21.25% (P < 0.001), 32% (P = 0.004), 2% (P = 0.6), 54% (P < 0.001) and 65% (P < 0.001) in groups Ia, Ib, IIa, IIb1, and IIb2, respectively. On the other hand the T (1/2) of RDR dropped significantly only in groups Ib, IIb1, and IIb2 by a mean of 6.5 min (P = 0.005), 8.02 min (P < 0.001), and 7.3 min (P < 0.001), respectively. CONCLUSION: TDJS in cases of EqUPJO with loin pain is helpful in defining cases suitable for intervention versus conservative treatment.

A comparative study between the Universal Spinal System® (USS) and the CD Horizon® Legacy™ (CDH) in the management of thoracolumbar fractures.

INTRODUCTION: For the treatment of unstable non-osteoporotic thoracolumbar fractures, the clinical and radiological outcome of short-segment fixation with the USS™ - Universal Spine System (DePuy Orthopedics, Inc., Warsaw, IN, USA) and the CD HORIZON® LEGACY™ 5.5 Spinal System, (Medtronic Sofamor Danek USA, Inc., Memphis, TN, USA) were compared. METHODS: From March 2015 to January 2016, 40 consecutive patients with unstable traumatic thoracolumbar fractures who met our inclusion criteria were treated with either the USS system or CDH Legacy system. Segmental kyphosis angle (SKA) and anterior body height (ABH) of fractured vertebrae, and ASIA Impairment Scale (AIS) were evaluated. Radiological fusion was confirmed with plain X-rays and when indicated with computerized tomography (CT). RESULTS: The mean immediate kyphotic angle correction was 16.6° for the Schanz and 6.4 for the Legacy system, and the immediate mean anterior vertebral body height correction was 0.92 cm for the Schanz and 0.51 cm for the Legacy system. Our study shows a significant statistical difference between Schanz and Legacy systems regarding post-operative segmental kyphosis and height correction immediately postoperatively, at 6 months and at one-year follow-up (p-value < 0.005). The degree of pain reduction and neurological improvement was not influenced by the screw system. CONCLUSION: Usage of USS in thoracolumbar fracture as a short-segment fixation led to a near anatomical reduction when compared to the Legacy system. However, there was no advantage regarding pain reduction and neurological outcome.

Single-stage anterior high sacrectomy for locally recurrent rectal cancer.

STUDY DESIGN: A review of prospectively collected data on a consecutive series of patients undergoing single-stage anterior high sacrectomy for locally recurrent rectal carcinoma (LRRC). OBJECTIVE: To determine the clinical outcome of patients who underwent anterior high sacrectomy for LRRC. SUMMARY OF BACKGROUND DATA: High sacrectomy for oncological resection remains technically challenging. Surgery has the potential to achieve cure in carefully selected patients. Complete (R0) tumor excision in LRRC may require sacrectomy. High sacral resections (S3 and above) typically require a combined anterior/supine and posterior/prone procedure. We investigated our experience performing single-stage anterior high sacrectomy for LRRC. METHODS: A consecutive series of patients with LRRC without systemic metastases who underwent resection with curative intent requiring high sacrectomy were identified. A review of a prospectively maintained colorectal and spine cancer database data was performed. An oblique dome high sacral osteotomy was performed during a single-stage anterior procedure. Outcome measures included surgical resection margin status, hospital length of stay, postoperative complications, physical functioning status, and overall survival. RESULTS: Nineteen consecutive patients were treated between 2002 and 2011. High sacrectomy was performed at sacral level S1-S2 in 4 patients, S2-S3 in 9 patients, and through S3 in 6 patients. An R0 resection margin was achieved histologically in all 19 cases. There was 1 early (<30 d) postoperative death (1/19, 5%). At median follow-up of 38 months, 13 patients had no evidence of residual disease, 1 was alive with disease, and 4 had died of disease. Morbidities occurred in 15 of the 19 patients (79%). CONCLUSION: Although high sacrectomy may require a combined anterior and posterior surgical approach, our series demonstrates the feasibility of performing single-stage anterior high sacrectomy in LRRC, with acceptable risks and outcomes compared with the literature. The procedure described by us for LRRC lessens the need for a simultaneous or staged prone posterior resection, with favorable R0 tumor resections, patient survival, and clinical outcomes. LEVEL OF EVIDENCE: N/A.

Ureterocalicostomy for treatment of complex cases of ureteropelvic junction obstruction in adults.

OBJECTIVES: To evaluate ureterocalicostomy (UC) in the management of selected cases of ureteropelvic junction (UPJ) obstruction. METHODS: The data from 22 patients who underwent UC from April 2002 to April 2009 were reviewed. The indications for UC were primary UPJ obstruction with completely intrarenal pelves (2 cases), complicated (secondary or recurrent) UPJ obstruction with an intrarenal pelvis and/or reversed caliceopelvic ratio (16 cases) and after iatrogenic injury at the UPJ (4 cases). The evaluation of treatment outcome was performed subjectively by symptom assessment and objectively using ultrasonography, radiography, and radioisotope diuretic renography. RESULTS: After a mean follow-up of 26.7±20.99 months, UC success was achieved in 16 patients (73%), with complete cure in 12 and improvement in 4. In 2, the condition remained stable, and failure occurred in 4 patients. Of these 4 patients, 2 underwent nephrectomy and 2 were treated with indwelling double-J stents with frequent exchanges. CONCLUSIONS: The results of UC in the present study were satisfactory. The procedure could be the best alternative in the case of complex UPJ obstruction in the presence of intrarenal pelvis and/or when the degree of calicectasis is greater than that of pyelectasis.