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Abstract Background Pathogenic BRCA1 or BRCA2 germline mutations contribute to hereditary breast, ovarian, prostate, and pancreatic cancer. Paradoxically, bi-allelic inactivation of BRCA1 or BRCA2 (bBRCA1/2) is embryonically lethal and decreases cellular proliferation. The compensatory mechanisms that facilitate oncogenesis in bBRCA1/2 tumors remain unclear. Methods We identified recurrent genetic alterations enriched in human bBRCA1/2 tumors and experimentally validated if these improved proliferation in cellular models. We analyzed mutations and copy number alterations (CNAs) in bBRCA1/2 breast and ovarian cancer from the TCGA and ICGC. We used Fisher’s exact test to identify CNAs enriched in bBRCA1/2 tumors compared to control tumors that lacked evidence of homologous recombination deficiency. Genes located in CNA regions enriched in bBRCA1/2 tumors were further screened by gene expression and their effects on proliferation in genome-wide CRISPR/Cas9 screens. A set of candidate genes was functionally validated with in vitro clonogenic survival and functional assays to validate their influence on proliferation in the setting of bBRCA1/2 mutations. Results We found that bBRCA1/2 tumors harbor recurrent large-scale genomic deletions significantly more frequently than histologically matched controls (n = 238 cytobands in breast and ovarian cancers). Within the deleted regions, we identified 277 BRCA1-related genes and 218 BRCA2-related genes that had reduced expression and increased proliferation in bBRCA1/2 but not in wild-type cells in genome-wide CRISPR screens. In vitro validation of 20 candidate genes with clonogenic proliferation assays validated 9 genes, including RIC8A and ATMIN (ATM-Interacting protein). We identified loss of RIC8A, which occurs frequently in both bBRCA1/2 tumors and is synthetically viable with loss of both BRCA1 and BRCA2. Furthermore, we found that metastatic homologous recombination deficient cancers acquire loss-of-function mutations in RIC8A. Lastly, we identified that RIC8A does not rescue homologous recombination deficiency but may influence mitosis in bBRCA1/2 tumors, potentially leading to increased micronuclei formation. Conclusions This study provides a means to solve the tumor suppressor paradox by identifying synthetic viability interactions and causal driver genes affected by large-scale CNAs in human cancers.

Abstract Given the chronic nature of schizophrenia, it is important to examine age-specific prevalence and incidence to understand the scope of the burden of schizophrenia across the lifespan. Estimates of lifetime prevalence of schizophrenia have varied widely and have often relied upon community-based data estimates from over two decades ago, while more recent studies have shown considerable promise by leveraging pooled datasets. However, the validity of measures of schizophrenia, particularly new onset schizophrenia, has not been well studied in these large health databases. The current study examines prevalence and validity of incidence measures of new diagnoses of schizophrenia in 2019 using two U.S. administrative health databases: MarketScan, a national database of individuals receiving employer-sponsored commercial insurance (N = 16,365,997), and NYS Medicaid, a large state public insurance program (N = 4,414,153). Our results indicate that the prevalence of schizophrenia is over 10-fold higher, and the incidence two-fold higher, in the NYS Medicaid population compared to the MarketScan database. In addition, prevalence increased over the lifespan in the Medicaid population, but decreased in the employment based MarketScan database beginning in early adulthood. Incident measures of new diagnoses of schizophrenia had excellent validity, with positive predictive values and specificity exceeding 95%, but required a longer lookback period for Medicaid compared to MarketScan. Further work is needed to leverage these findings to develop robust clinical outcome predictors for new onset of schizophrenia within large administrative health data systems.

The development of abstractive summarization methods is a crucial task in Natural Language Processing (NLP) that presents challenges, which require the creation of intelligent systems that are capable of extracting the main idea from text effectively and generate coherent summary. Numerous existing abstractive approaches do not take into account the importance of the broader context or fail to capture the global semantics in identifying salient content for summary. Moreover, there is lack of studies that extensively evaluated abstractive summarization models for specific domains, such as medical scientific document summarization. With this motivation behind, this paper developed an integrated framework for abstractive summarization of medical scientific documents that integrates topic-aware Heterogeneous Graph Neural Network with a Transformer model. The suggested framework uses Latent Dirichlet Allocation (LDA) for topic modeling to uncover latent topics and global information, thus preserving document-level attributes important for creation of effective summaries. In addition to topic modeling, the framework utilized a Heterogeneous Graph Neural Network (HGNN), capable of capturing the relationship between sentences through graph-based document representation, and allows for the concurrent updating of both local and global information. Finally, the framework is integrated with a Transformer decoder, which greatly enhances the ability of model to produce accurate and informative abstractive summaries. The performance of proposed framework is evaluated on publicly available PubMed dataset related to medical scientific papers. Experimental results illustrate that the suggested framework for abstractive summarization showed superior performance as compared to the state-of-the-art models, achieving high F1-Scores: 46.03 for Rouge-1, 21.42 for Rouge-2, and 39.71 for Rouge-L. Our research makes a significant contribution to the field of natural language processing, particularly in the area of medical scientific document summarization. It demonstrates superior performance and provides a deeper understanding of document structure, and has the potential to impact various applications by offering efficient access to information.

The widespread use of the Internet and the tremendous growth of social media have enabled people to connect with each other worldwide. Individuals are free to express themselves online, sharing their photos, videos, and text messages globally. However, such freedom sometimes leads to misuse, as some individuals exploit this platform by posting hateful and abusive comments on forums. The proliferation of abusive language on social media negatively impacts individuals and groups, leading to emotional distress and affecting mental health. It is crucial to automatically detect and filter such abusive content in order to effectively tackle this challenging issue. Detecting abusive language in text messages is challenging due to intentional word concealment and contextual complexity. To counter abusive speech on social media, we need to explore the potential of machine learning (ML) and deep learning (DL) models, particularly those equipped with attention mechanisms. In this study, we utilized popular ML and DL models integrated with attention mechanism to detect abusive language in Urdu text. Our methodology involved employing Count Vectorizer and Term Frequency-Inverse Document Frequency (TF/IDF) to extract n-grams at the word level: Unigrams (Uni), Bigrams (Bi), Trigrams (Tri), and their combination (Uni + Bi + Tri). Initially, we evaluated four traditional ML models—Logistic Regression (LR), Gaussian Naïve Bayes (NB), Support Vector Machine (SVM), and Random Forest (RF)—on both proposed and established datasets. The results highlighted that RF model outperformed other conventional models in terms of accuracy, precision, recall, and F1-measure on both datasets. In our implementation of deep learning models, we employed various models integrated with custom fastText and Word2Vec embeddings, each equipped with an attention layer, except for the Convolutional Neural Network (CNN). Our findings indicated that the Bidirectional Long Short-Term Memory (Bi-LSTM) + attention model, utilizing custom Word2Vec embeddings, exhibited improved performance in detecting abusive language on both datasets.

AbstractRural poverty is often discussed in combination with spatial inequality where rural poor face worse problems with diverse remedies in comparison to urban poor. The present study aims to find out area-level effects and possible determinants of rural poverty based on data from the Rural Household Panel Survey of Pakistan Round-1 (2012). The multilevel binary logistic model is used to integrate concurrent individual and district-level variables to explore area-level effects on poverty. A two-level multilevel model is used for the analysis of the poverty status of 4804 respondents nested in 19 districts. Individual-level variables included in the model are education, socioeconomic status, and spending behaviour. It is found that approximately 60% of individuals of the overall population were found poor, and district-level effect accounts for 7% variations in poverty. A particularly significant ratio of poor is found for people who attained deeni madrassa/religious education or no education at all. Poverty existence is significantly associated with spending behavior and socioeconomic status. The persons with relatively middle/high socioeconomic status and successful educational achievement but a lacking attitude toward spending money are considered poor.

Objective: To evaluate the effect of Ajwa dates pit powder (ADP) on lipid profile, body composition and blood pressure in patients with hyperlipidemia.Materials and Methods: This randomized controlled clinical study was carried out on 40 patients with total cholesterol >200 mg/dl, triglycerides >150 mg/dl and BMI >25, of either sex, aged 30-50 years, who were recruited through written consent. The patients were divided into two groups (n=20 each): the ADP and the control group (CG). All patients received the doctor’s prescribed class A statin (Rosuvastatin/ Atorvastatin) 10 mg/day, while 2.7 g ADP was given on daily basis before breakfast with lukewarm water for 40 days and the control group received the same amount of wheat flour. Body composition, blood pressure and lipid profile were determined at baseline, and after 20 and 40 days. Data were analyzed by using SPSS and GraphPad Prism.Results: ADP significantly reduced body weight (p

Abstract Schizophrenia is among the leading causes of disability worldwide. Prior studies have conclusively demonstrated that the etiology of schizophrenia contains a strong genetic component. However, the understanding of environmental contributions and gene–environment interactions have remained less well understood. Here, we estimated the genetic and environmental contributions to schizophrenia risk using a unique combination of data sources and mathematical models. We used the administrative health records of 481,657 U.S. individuals organized into 128,989 families. In addition, we employed rich geographically specific measures of air, water, and land quality across the United States. Using models of progressively increasing complexity, we examined both linear and non-linear contributions of genetic variation and environmental exposures to schizophrenia risk. Our results demonstrate that heritability estimates differ significantly when gene–environment interactions are included in the models, dropping from 79% for the simplest model, to 46% in the best-fit model which included the full set of linear and non-linear parameters. Taken together, these findings suggest that environmental factors are an important source of explanatory variance underlying schizophrenia risk. Future studies are warranted to further explore linear and non-linear environmental contributions to schizophrenia risk and investigate the causality of these associations.

Over the last two decades, social media platforms have grown dramatically. Twitter and Facebook are the two most popular social media platforms, with millions of active users posting billions of messages daily. These platforms allow users to have freedom of expression. However, some users exploit this facility by disseminating hate speeches. Manual detection and censorship of such hate speeches are impractical; thus, an automatic detection mechanism is required to detect and counter hate speeches in a real-time environment. Most research in hate speech detection has been carried out in the English language. Still, minimal work has been explored in other languages, mainly Urdu written in Roman Urdu script. A few research have attempted machine learning, and deep learning models for Roman Urdu hate speech detection; however, due to a scarcity of Roman Urdu resources, and a large corpus with defined annotation rules, a robust hate speech detection model is still required. With this motivation, this study contributes in the following manner: we developed annotation guidelines for Roman Urdu Hate Speech. Second, we constructed a new Roman Urdu Hate Speech Dataset (RU-HSD-30K) that was annotated by a team of experts using the annotation rules. To the best of our knowledge, the Bi-LSTM model with an attention layer for Roman-Urdu Hate Speech Detection has not been explored. Therefore, we developed a context-aware Roman Urdu Hate Speech detection model based on Bi-LSTM with an attention layer and used custom word2vec for word embeddings. Finally, we examined the effect of lexical normalization of Roman Urdu words on the performance of the proposed model. Different traditional as well as deep learning models, including LSTM and CNN models, were used as baseline models. The performance of the models was assessed in terms of evaluation metrics like accuracy, precision, recall, and F1-score. The generalization of each model is also evaluated on a cross-domain dataset. Experimental results revealed that Bi-LSTM with attention outperformed the traditional machine learning models and other deep learning models with an accuracy score of 0.875 and an F-Score of 0.885. In addition, the results demonstrated that our suggested model (Bi-LSTM with Attention Layer) is more general than previous models when applied to unseen data. The results confirmed that lexical normalization of Roman Urdu words enhanced the performance of the suggested model.

Since the start of this century, much attention has been given to economic growth and environmental changes and their effects on human beings. The present study has developed a comprehensive model to discuss the nexus between economic growth, environmental degradation, and public health. Furthermore, renewable energy consumption and public health spending are used as moderators to make the model more inclusive. The time series data from 1972 to 2020 has been used, and a regression path modeling tool SPPS-PROCESS Model 29, has been applied to carry out the results. The results showed a positive and significant effect of economic growth on environmental degradation, while renewable energy consumption reduces environmental degradation. Furthermore, environmental degradation is negatively affecting the health status in Pakistan. The results of the total effects showed that economic growth positively contributes to public health with a low coefficient. The indirect conditional impact of economic growth on human health through the mediating role of environmental degradation becomes positive from negative in the long run due to renewable energy and public spending on health. Based on the result, some policies are suggested in the last section of this study.

Purpose: : Guidelines for early-stage breast cancer allow for radiation therapy (RT) omission after breast conserving surgery among older women, though high utilization of RT persists. This study explored surgeon referral and the effect of a productivity-based bonus metric for radiation oncologists in an academic institution with centralized quality assurance review. Methods and materials: : We evaluated patients ≥70 years of age treated with breast conserving surgery for estrogen receptor (ER)+ pT1N0 breast cancer at a single tertiary cancer network between 2015 and 2018. The primary outcomes were radiation oncology referral and RT receipt. Covariables included patient and physician characteristics and treatment decisions before versus after productivity metric implementation. Univariable generalized linear effects models explored associations between these outcomes and covariables. Results: : Of 703 patients included, 483 (69%) were referred to radiation oncology and 273 (39%) received RT (among those referred, 57% received RT). No difference in RT receipt pre- versus post-productivity metric implementation was observed (P = .57). RT receipt was associated with younger patient age (70-74 years; odds ratio [OR], 2.66; 95% confidence interval [CI], 1.54-4.57) and higher grade (grade 3; OR, 7.75; 95% CI, 3.33-18.07). Initial referral was associated with younger age (70-74; OR, 5.64; 95% CI, 3.37-0.45) and higher performance status (Karnofsky performance status ≥90; OR, 5.34; 95% CI, 2.63-10.83). Conclusions: : Nonreferral to radiation oncology accounted for half of RT omission but was based on age and Karnofsky performance status, in accordance with guidelines. Lack of radiation oncologist practice change in response to misaligned financial incentives is reassuring, potentially reflecting incentive design and/or centralized quality assurance review. Multi-institutional studies are needed to confirm these findings.

The present study is designed to examine the relationship between wage inequalities and economic prosperity in the case of Pakistan. Using provincial-level data for the years 2000 to 2020, the study estimated a multivariate regression model by employing Auto Regressive Distributive Lag (ARDL) pooled mean group (PMG) technique. The results reveal that wage inequality, government development spending, labor force participation, and human development significantly affect economic prosperity. It is concluded that gender disparity in the labor market is the main hurdle in the economic wellbeing of the masses in the country. Reducing the differences in wages will enhance overall economic prosperity. The government and private sector should take collaborative measures to reduce wage disparities between the male and female workforce. The study also suggests that government should increase development expenditure, especially on health, education, and social infrastructure, to increase economic prosperity.

Social media applications, such as Twitter and Facebook, allow users to communicate and share their thoughts, status updates, opinions, photographs, and videos around the globe. Unfortunately, some people utilize these platforms to disseminate hate speech and abusive language. The growth of hate speech may result in hate crimes, cyber violence, and substantial harm to cyberspace, physical security, and social safety. As a result, hate speech detection is a critical issue for both cyberspace and physical society, necessitating the development of a robust application capable of detecting and combating it in real-time. Hate speech detection is a context-dependent problem that requires context-aware mechanisms for resolution. In this study, we employed a transformer-based model for Roman Urdu hate speech classification due to its ability to capture the text context. In addition, we developed the first Roman Urdu pre-trained BERT model, which we named BERT-RU. For this purpose, we exploited the capabilities of BERT by training it from scratch on the largest Roman Urdu dataset consisting of 173,714 text messages. Traditional and deep learning models were used as baseline models, including LSTM, BiLSTM, BiLSTM + Attention Layer, and CNN. We also investigated the concept of transfer learning by using pre-trained BERT embeddings in conjunction with deep learning models. The performance of each model was evaluated in terms of accuracy, precision, recall, and F-measure. The generalization of each model was evaluated on a cross-domain dataset. The experimental results revealed that the transformer-based model, when directly applied to the classification task of the Roman Urdu hate speech, outperformed traditional machine learning, deep learning models, and pre-trained transformer-based models in terms of accuracy, precision, recall, and F-measure, with scores of 96.70%, 97.25%, 96.74%, and 97.89%, respectively. In addition, the transformer-based model exhibited superior generalization on a cross-domain dataset.

The Web Search Engine (WSE) is a software system used to retrieve data from the web successfully. WSE uses the user’s search queries to build the user’s profile and provide personalized results. Users’ search queries hold identifiable information that could compromise the privacy of the respective user. This work proposes a multi-group distributed privacy-preserving protocol (MG-OSLo) and tries to investigate the state-of-the-art distributed privacy-preserving protocols for computing web search privacy. The MG-OSLo comprises multiple groups in which each group has a fixed number of users. The MG-OSLo measures the impact of the multi-group on the user’s privacy. The primary objective of this work is to assess local privacy and profile privacy. It aims at evaluating the impact of group size and group count on a user’s privacy. Two grouping approaches are used to group the users in MG-OSLo, i.e. a non-overlapping group design and overlapping group design. The local privacy results reveal that the probability of linking a query to the user depends on the group size and group count. The higher the group size or group count, the lower the likelihood of relating the query to the user. The profile privacy computes the profile obfuscation level using a privacy metric Profile Exposure Level (PEL). Different experiments have been performed to compute the profile privacy of the subset of an AOL query log for two situations: i) self-query submissions allowed and ii) self-query submissions not allowed. The privacy achieved by MG-OSLo is compared with the modern privacy-preserving protocol UUP(e), OSLo, and Co-utile protocols. The results show that the MG-OSLo provided better results as compared to OSLo, UUP, and Co-utile. Similarly, the multi-group has a positive impact on local privacy and user profile privacy.

Percutaneous nephrostomy insertion is a technique performed by an interventional radiologist or a urologist for an acutely or long-standing obstructed urinary tract. Mastering the technique involves overcoming a steep learning curve. Various methods of training have been developed over the years to facilitate learning. These vary from simple physical models, such as biological or non-biological practice phantoms, to more sophisticated virtual reality sets, which allow for a more lifelike learning environment by replicating factors such as kidney movement caused by breathing. The authors discuss the pros and cons of different practice models and the challenges that trainees face on their journey to becoming competent at performing nephrostomies. They also propose their recommendations based on the experience of trainees in their institution.

Introduction: Health care workers are found to be at three times greater risk of getting infected as compared to the general public. Scientists and doctors all over the world have agreed upon the use of PPE including gloves, masks, head covers, face shields, goggles, and jumpsuits in protection against COVID-19. Materials and Methods: This observational prospective study was conducted in the surgical emergency of Holy Family Hospital, Rawalpindi over a period of 2 months and 21 days. Patients included all those who presented to surgical emergency with suspicion of being positive for COVID-19 and time taken by first-line health care workers in attending them. 157 patients were observed for this purpose and 23 first-line surgeons including general, orthopedic, and neurosurgeons were interviewed regarding their fears and concerns about contracting COVID-19 and infecting their families. Results: It was observed that a surgeon took on an average of 10 minutes (+/-3 minutes) in wearing all the personal protective equipment and a total of 14minutes (+/- 5 minutes) in reaching a patient in the trauma room with symptoms suggestive of COVID-19. This was in contrast to a patient presenting to a trauma room who had no respiratory symptoms or fever, in which case, the patient was seen within 3 minutes (+/- 2 minutes) of presentation to a surgical emergency. Out of 23 surgeons, 15 had reasonably aware of the disease while 7 were knowledgeable up to the mark. 17 surgeons were extremely fearful about contracting the disease and infecting their friends and families. 7 surgeons confessed to avoiding COVID-19 patients and 9 surgeons confessed that they commanded their junior surgeons to see suspected COVID-19 patients in the emergency room. Conclusion: We concluded that delay in attending trauma patients suspected of being positive for COVID-19 was a worrisome problem that needed to be addressed. Numerous local and regional circumstances served as a factor for this delay, most important of which came out to be an inadequate provision of PPE, time consumed in collecting and wearing PPE, fear of the disease, and anxiety provoked due to this fear among surgeons.

The IoT devices produce a massive amount of content compared to the host-centric (IP-based) network. The IP-based network has no strengthened capabilities to handle the growing traffic of the IoT network. The existing research is focusing on proposing Information Centric Network (ICN) framework with potential replacement of IP-based network. Using the caching technique in ICN based IoT significantly strengthen the resource-constrained IoT devices, by reducing the access time with optimized energy consumption. Thus, this paper proposes a caching scheme for IoT contents, known as Central Control Caching (CCC) Scheme. This scheme positions a central entity between different autonomous systems that manage caching and updates the content, by keeping information in a table regarding different contents present on different autonomous systems. This information is utilized when cached contents are exchanged among different autonomous systems. Furthermore, when contents becomes stale, the same information is used to identify the autonomous systems with a copy of the outdated content. The simulation results show that the proposed scheme has outperformed in terms of reduced energy consumption, response latency, and cache-hit compared to state-of-the-art works.

To recognize the basis of disease, it is essential to determine its underlying genes. Understanding the association between underlying genes and genetic disease is a fundamental problem regarding human health. Identification and association of genes with the disease require time consuming and expensive experimentations of a great number of potential candidate genes. Therefore, the alternative inexpensive and rapid computational methods have been proposed that can identify the candidate gene associated with a disease. Most of these methods use phenotypic similarities due to the fact that genes causing same or similar diseases have less variation in their sequence or network properties of protein-protein interactions based on-premises that genes lie closer in protein interaction network that causes the similar or same disease. However, these methods use only basic network properties or topological features and gene sequence information or biological features as a prior knowledge for identification of gene-disease association, which restricts the identification process to a single gene-disease association. In this study, we propose and analyze some novel computational methods for the identification of genes associated with diseases. Some advance topological and biological features that are overlooked currently are introducing for identifying candidate genes. We evaluate different computational methods on disease-gene association data from DisGeNET in a 10-fold cross-validation mode based on TP rate, FP rate, precision, recall, F-measure, and ROC curve evaluation parameters. The results reveal that various computational methods with advanced feature set outperform previous state-of-the-art techniques by achieving precision up to 93.8%, recall up to 93.1%, and F- measure up to 92.9%. Significantly, we apply our methods to study four major diseases: Thalassemia, Diabetes, Malaria, and Asthma. Simulation results show that the proposed Deep Extreme Learning Machine (DELM) gives more accurate results as compared to previously published approaches.

With the increased usage of Web 2.0 and data-affluent tools such as social media platforms and web blog services, the challenge of extracting public sentiment and disseminating personal health information has become more common than ever in the last decade. This paper proposes a novel model for Dengue disease detection based on social media posts alone. The model does not access any personal information of people or any medical record. The model extracts the presence of patients infected with Dengue disease based on tweets only and decides whether it is a general discussion about the disease, and no one is actually infected, or people are actually infected with that disease. The identification of people infected with Dengue is determined by clinical tests, but the propose technique is used for automatic surveillance and identification of regions where the spread is happening at an alarming rate and guide healthcare professional to take necessary actions to control the spread. This paper uses different machine/deep learning approaches to utilize tweets data for automatic and efficient disease detection. Experimental results demonstrate that the proposed model is able to achieve 92% accuracy compared to the current state-of-the-art techniques in this domain.

Ureteral stents are routinely used in urological practice for many indications including obstruction of ureter, ureteral stricture, prior to treatment with extracorporeal shock wave lithotripsy, and to promote healing following ureteral injury. Complications reported with ureteric stents include stent migration, stent rupture, encrustation, ureteral perforation, erosion, and fistulation. Knotting of an indwelling ureteral stent is a very rare complication, with fewer than 30 cases reported in the literature. Techniques for managing this complication include using a holmium laser to cut the knot, percutaneous antegrade removal, and gentle traction. We describe the case of a knotted stent and its removal along with a comprehensive literature review.

Purpose: The 2016 American Society for Radiation Oncology consensus guidelines for the use of accelerated partial-breast irradiation (APBI) define “suitable,” “cautionary,” and “unsuitable” populations for this adjuvant breast radiation therapy technique. We sought to determine whether patients in the cautionary group exhibited adverse outcomes after APBI compared with their suitable counterparts. Methods and Materials: We identified 252 consecutively treated patients from a single institution with in situ or early-stage invasive breast cancer who underwent APBI between 2008 and 2017. Treatment technique was uniform throughout the population, consisting of 3-dimensional conformal radiation therapy to 40 Gy administered in 10 daily fractions. Results: One hundred seventy-eight patients (70%) were classified as suitable, 69 (27%) as cautionary, and 5 (2.0%) as unsuitable. Because unsuitable patients were few and had no recurrences, they were excluded from analysis. At a median follow-up time of 3.9 years, 97.2% of patients were free of recurrence. Four patients (1.5% overall; 3 suitable and 1 cautionary) experienced ipsilateral in-breast recurrences, and 1 cautionary patient developed an ipsilateral regional recurrence in an axillary lymph node. There was no significant difference in the rate of ipsilateral breast recurrence (2.4% vs 1.0%) between cautionary and suitable groups. Conclusions: Local recurrences are rare among guideline-defined cautionary patients with in situ or invasive breast cancer treated with APBI delivered via daily 3-dimensional conformal radiation therapy to 40 Gy. At a median follow-up of 3.9 years, no significant differences in local control were noted between cautionary and suitable patient groups. Further study is needed to characterize long-term disease outcomes among various risk groups.

The present study is designed to investigate that how Economic Growth (EG) of the South Asian region has affected the Environmental Degradation (ED) from 1980 to 2018. This study has used Newey and West (1987) robust standard errors approach to overcome the problem of autocorrelation and heteroskedasticity in panel data. The results of the statistical model confirmed the existence of the Inverted U-shaped Environmental Kuznets Curve (EKC). Furthermore, the results also confirmed that the use of energy is also deteriorating the environment significantly. One significant contribution of the study is to check the causality between EG and CO2 by applying a relatively new approach namely Granger non-causality test presented by Dumitrescu and Hurlin (2012). The results confirmed that economic growth is contributing towards more CO2 emissions. The study concluded that South Asian countries should use environment-friendly renewable energy sources to achieve a higher growth rate.

Today, the entire world is battling to contain the spread of COVID-19. Massive efforts are being made to find a therapeutic solution in the shortest possible time. However, the research community is becoming increasingly concerned about taking a shortsighted strategy without contemplating the long-term consequences. For example, It has been reported that only 8.4% of total COVID-19 patients develop a secondary bacterial infection. In comparison, 74.6% of them are administered with antibiotics as prophylactic treatment. We contend that overuse of broad-spectrum antibiotics increases the likelihood of AMR development and negatively affects the patient's recovery due to the prevalence of the ''gut-lung axis.''. Consequently, the use of antibiotics to treat COVID-19 patients must be rationalized, or an alternative treatment must be sought that does not risk contributing to AMR development and positively impacts the treatment outcomes. Phage therapy, a century-old concept, is one of the most promising approaches that can be adapted to serve this purpose. This review emphasizes the negative impact of excessive antibiotic use in COVID-19 treatment and provides an overview of how phage therapy can be used as an alternative treatment option. We have argued that targeted killing (narrow spectrum) and anti-inflammatory (which can target the primary cause of mortality in COVID-19) properties of phages can be an effective alternative to antibiotics.

Summary: The highly lethal brain cancer glioblastoma (GBM) poses a daunting challenge because the blood-brain barrier renders potentially druggable amplified or mutated oncoproteins relatively inaccessible. Here, we identify sphingomyelin phosphodiesterase 1 (SMPD1), an enzyme that regulates the conversion of sphingomyelin to ceramide, as an actionable drug target in GBM. We show that the highly brain-penetrant antidepressant fluoxetine potently inhibits SMPD1 activity, killing GBMs, through inhibition of epidermal growth factor receptor (EGFR) signaling and via activation of lysosomal stress. Combining fluoxetine with temozolomide, a standard of care for GBM, causes massive increases in GBM cell death and complete tumor regression in mice. Incorporation of real-world evidence from electronic medical records from insurance databases reveals significantly increased survival in GBM patients treated with fluoxetine, which was not seen in patients treated with other selective serotonin reuptake inhibitor (SSRI) antidepressants. These results nominate the repurposing of fluoxetine as a potentially safe and promising therapy for patients with GBM and suggest prospective randomized clinical trials.

Seasonal affective disorder (SAD) famously follows annual cycles, with incidence elevation in the fall and spring. Should some version of cyclic annual pattern be expected from other psychiatric disorders? Would annual cycles be similar for distinct psychiatric conditions? This study probes these questions using 2 very large datasets describing the health histories of 150 million unique U.S. citizens and the entire Swedish population. We performed 2 types of analysis, using "uncorrected" and "corrected" observations. The former analysis focused on counts of daily patient visits associated with each disease. The latter analysis instead looked at the proportion of disease-specific visits within the total volume of visits for a time interval. In the uncorrected analysis, we found that psychiatric disorders' annual patterns were remarkably similar across the studied diseases in both countries, with the magnitude of annual variation significantly higher in Sweden than in the United States for psychiatric, but not infectious diseases. In the corrected analysis, only 1 group of patients-11 to 20 years old-reproduced all regularities we observed for psychiatric disorders in the uncorrected analysis; the annual healthcare-seeking visit patterns associated with other age-groups changed drastically. Analogous analyses over infectious diseases were less divergent over these 2 types of computation. Comparing these 2 sets of results in the context of published psychiatric disorder seasonality studies, we tend to believe that our uncorrected results are more likely to capture the real trends, while the corrected results perhaps reflect mostly artifacts determined by dominantly fluctuating, health-seeking visits across a given year. However, the divergent results are ultimately inconclusive; thus, we present both sets of results unredacted, and, in the spirit of full disclosure, leave the verdict to the reader.

With the development of wireless technology, two basic wireless network models that are commonly used, known as infrastructure and wireless ad hoc networks (WANETs), have been developed. In the literature, it has been observed that channel contention is one of the main reasons for packet drop in WANETs. To handle this problem, this paper presents a routing protocol named CCBR (Channel Contention Based Routing). CCBR tries to determine a least contended path between the endpoints to increase packet delivery ratio and to reduce packet delay and normalized routing overhead. Moreover, throughout the active data section, each intermediate node computes its channel contention value. If an intermediate node detects an increase in channel contention, it notifies the source node. Then the source node determines another least contended route for transmission. The advantages of CCBR are verified in our NS2-based performance study, and the results show that CCBR outperforms ad hoc on-demand distance vector (AODV) in terms of packet delivery ratio, end-to-end delay, and routing overhead by 4% to 9%.

Acute Myeloid Leukemia (AML) is a kind of fatal blood cancer with a high death rate caused by abnormal cells’ rapid growth in the human body. The usual method to detect AML is the manual microscopic examination of the blood sample, which is tedious and time-consuming and requires a skilled medical operator for accurate detection. In this work, we proposed an AlexNet-based classification model to detect Acute Myeloid Leukemia (AML) in microscopic blood images and compared its performance with LeNet-5-based model in Precision, Recall, Accuracy, and Quadratic Loss. The experiments are conducted on a dataset of four thousand blood smear samples. The results show that AlexNet was able to identify 88.9% of images correctly with 87.4% precision and 98.58% accuracy, whereas LeNet-5 correctly identified 85.3% of images with 83.6% precision and 96.25% accuracy.

Web search engines usually keep users’ profiles for multiple purposes, such as result ranking and relevancy, market research, and targeted advertisements. However, user web search history may contain sensitive and private information about the user, such as health condition, personal interests, and affiliations that may infringe users’ privacy since a user’s identity may be exposed and misused by third parties. Numerous techniques are available to address privacy infringement, including Private Information Retrieval (PIR) protocols that use peer nodes to preserve privacy. Previously, we have proved that PIR protocols are vulnerable to the QuPiD Attack. In this research, we proposed NN-QuPiD Attack, an improved version of QuPiD Attack that uses an Artificial Neural Network (RNN) based model to associate queries with their original users. The results show that the NN-QuPiD Attack gave 0.512 Recall with the Precision of 0.923, whereas simple QuPiD Attack gave 0.49 Recall with the Precision of 0.934 with the same data.

Online forums have become the main source of knowledge over the Internet as data are constantly flooded into them. In most cases, a question in a web forum receives several responses, making it impossible for the question poster to obtain the most suitable answer. Thus, an important problem is how to automatically extract the most appropriate and high-quality answers in a thread. Prior studies have used different combinations of both lexical and nonlexical features to retrieve the most relevant answers from discussion forums, and hence, there is no standard/general set of features that could be effectively used for relevant answer/reply post classification. However, this study proposed an answer detection model that is exclusively relying on lexical features and employs a random forest classifier for classification of answers in discussion boards. Experimental results showed that the proposed answer detection model outperformed the baseline technique and other state-of-the-art machine learning algorithms in terms of classification accuracy on benchmark forum datasets.

Seasonal outbreaks have several different periods that occur primarily during winter in temperate regions, while influenza may occur throughout the year in tropical regions, triggering outbreaks more irregularly. Similarly, dengue occurs in the star of the rainy season in early May and reaches its peak in late June. Dengue and flu brought an impact on various countries in the years 2017–2019 and streaming Twitter data reveals the status of dengue and flu outbreaks in the most affected regions. This research work presents that Social Media Analysis (SMA) can be used as a detector of the epidemic outbreak and to understand the sentiment of social media users regarding various diseases. Providing awareness about seasonal outbreaks through SMA is an effective approach for researchers and healthcare responders to detect the early outbreaks. The proposed model aims to find the sentiment about the disease in tweets, and the seasonal outbreaks-related tweets are classified into two classes as disease positive and disease negative. This work proposes a machine-learning-based approach to detect dengue and flu outbreaks in social media platform Twitter, using four machine learning algorithms: Random Forest (RF), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Decision Tree (DT), with the help of Term Frequency and Inverse Document Frequency (TF-IDF). For experimental analysis, two datasets (dengue and flu) are analyzed individually. The experimental results show that the RF classifier has outperformed the comparison models in terms of improved accuracy, precision, recall, F1-measure, and Receiver Operating Characteristic (ROC) curve. The proposed work offers favorable performance with total precision, accuracy, recall, and F1-measure ranging from 84% to 88% for conventional machine learning techniques.

Membranes for carbon dioxide permeation have been recognized as potential candidates for CO2 separation technology, particularly in the energy sector. Supported molten-salt membranes provide ionic routes to facilitate carbon dioxide transport across the membrane, permit the use of membrane at higher temperature, and offer selectivity based on ionic affinity of targeted compound. In this review, molten-carbonate ceramic membranes have been evaluated for CO2 separation. Various research studies regarding mechanisms of permeation, properties of molten salt, significance of material selection, geometry of support materials, and surface modifications have been assessed with reference to membrane stabilities and operational flux rates. In addition, the outcomes of permeation experiments, stability tests, selection of the compatible materials, and the role of interfacial reactions for membrane degradation have also been discussed. At the end, major challenges and possible solutions are highlighted along with future recommendations for fabricating efficient carbon dioxide separation membranes.

Drug-induced thrombocytopenia (DIT) is a differential diagnosis for consideration when acute thrombocytopenia is encountered in the outpatient or inpatient setting. The mechanism of thrombocytopenia induced by different antiplatelet therapies varies. DIT may occur due to antibody formation following the exposure to a drug, or naturally occurring preexisting antibodies may produce rapid-onset thrombocytopenia when a drug molecule binds to a platelet receptor inducing a conformational change thus rendering it to be an antigen target for naturally occurring antibodies. A 66-year-old female with history of hypertension presented with non-ST elevation myocardial infarction, had drug eluting stent placed in first obtuse marginal artery of left circumflex coronary artery. Started on antiplatelet medications aspirin 81 mg, ticagrelor 90 mg (which was later transitioned to clopidogrel 75 mg), as well as tirofiban 12.5 mg (for 12 hours only). Tirofiban is a GP IIb/IIIa antagonist, other drugs in this class have been documented to induce thrombocytopenia as well, but rates for tirofiban appear to be the highest, the reason is unclear. These antibodies are thought to be either naturally occurring or induced from conformational changes to GP IIb/IIIa binding site after binding to the GP IIb/IIIa receptor, binding of these drugs to the receptor precipitates an epitope much more specific for platelet surface antigens. Tirofiban and clopidogrel/ticagrelor can cause thrombocytopenia, but onset in this case is unusual: acute antibody reaction would be expected within hours, not delayed 30 hours after starting antiplatelet medication, and nonacute reaction would present 1 to 2 weeks out.

The transition to daylight saving time (DST) is beneficial for energy conservation but at the same time it has been reported to increase the risk of cerebrovascular and cardiovascular problems. Here, we evaluate the effect of the DST shift on a whole spectrum of diseases-an analysis we hope will be helpful in weighing the risks and benefits of DST shifts. Our study relied on a population-based, cross-sectional analysis of the IBM Watson Health MarketScan insurance claim dataset, which incorporates over 150 million unique patients in the US, and the Swedish national inpatient register, which incorporates more than nine million unique Swedes. For hundreds of sex- and age-specific diseases, we assessed effects of the DST shifts forward and backward by one hour in spring and autumn by comparing the observed and expected diagnosis rates after DST shift exposure. We found four prominent, elevated risk clusters, including cardiovascular diseases (such as heart attacks), injuries, mental and behavioral disorders, and immune-related diseases such as noninfective enteritis and colitis to be significantly associated with DST shifts in the United States and Sweden. While the majority of disease risk elevations are modest (a few percent), a considerable number of diseases exhibit an approximately ten percent relative risk increase. We estimate that each spring DST shift is associated with negative health effects-with 150,000 incidences in the US, and 880,000 globally. We also identify for the first time a collection of diseases with relative risks that appear to decrease immediately after the spring DST shift, enriched with infections and immune system-related maladies. These diseases' decreasing relative risks might be driven by the documented boosting effect of a short-term stress (such as that experienced around the spring DST shift) on the immune system.

In mobile ad hoc networks (MANETs), the topology differs very often due to mobile nodes (MNs). The flat network organization has high topology maintenance messages overload. To reduce this message overload in MANET, clustering organizations are recommended. Grouping MANET into MNs has the advantage of controlling congestion and easily repairing the topology. When the MANET size is large, clustered MN partitioning is a multiobjective optimization problem. Several evolutionary algorithms such as genetic algorithms (GAs) are used to divide MANET into clusters. GAs suffer from premature convergence. In this article, a clustering algorithm based on a memetic algorithm (MA) is proposed. MA uses local exploration techniques to reduce the likelihood of early convergence. The local search function in MA is to find the optimal local solution before other evolutionary algorithms. The optimal clusters in MANET can be achieved using MA for dynamic load balancing. In this work, the network is considered a graph G (V, E), where V represents MN and E represent the communication links of the neighboring MNs. The aim of this study is to find the cluster headset (CH) as early as possible when needed. High-quality individuals are selected for the new population in the next generation. New individuals are generated using the crossover mechanism on the chromosome once the two parents have been selected. Data are communicated via CHs between other clusters. The proposed technique is compared with existing techniques such as DGAC, MobHiD, and EMPSO. The proposed technique overcomes the state-of-the-art clustering schemes in terms of cluster counting, reaffiliation rate, cluster life, and overload of control messages.

Huge data on the web come from discussion forums, which contain millions of threads. Discussion threads are a valuable source of knowledge for Internet users, as they have information about numerous topics. The discussion thread related to single topic comprises a huge number of reply posts, which makes it hard for the forum users to scan all the replies and determine the most relevant replies in the thread. At the same time, it is also hard for the forum users to manually summarize the bulk of reply posts in order to get the gist of discussion thread. Thus, automatically extracting the most relevant replies from discussion thread and combining them to form a summary are a challenging task. With this motivation behind, this study has proposed a sentence embedding based clustering approach for discussion thread summarization. The proposed approach works in the following fashion: At first, word2vec model is employed to represent reply sentences in the discussion thread through sentence embeddings/sentence vectors. Next, K-medoid clustering algorithm is applied to group semantically similar reply sentences in order to reduce the overlapping reply sentences. Finally, different quality text features are utilized to rank the reply sentences in different clusters, and then the high-ranked reply sentences are picked out from all clusters to form the thread summary. Two standard forum datasets are used to assess the effectiveness of the suggested approach. Empirical results confirm that the proposed sentence based clustering approach performed superior in comparison to other summarization methods in the context of mean precision, recall, and F-measure.

Efficient community detection in a complex network is considered an interesting issue due to its vast applications in many prevailing areas such as biology, chemistry, linguistics, social sciences, and others. There are several algorithms available for network community detection. This study proposed the Sigmoid Fish Swarm Optimization (SiFSO) algorithm to discover efficient network communities. Our proposed algorithm uses the sigmoid function for various fish moves in a swarm, including Prey, Follow, Swarm, and Free Move, for better movement and community detection. The proposed SiFSO algorithm’s performance is tested against state-of-the-art particle swarm optimization (PSO) algorithms in Q-modularity and normalized mutual information (NMI). The results showed that the proposed SiFSO algorithm is 0.0014% better in terms of Q-modularity and 0.1187% better in terms of NMI than the other selected algorithms.

Nowadays, online product reviews have been at the heart of the product assessment process for a company and its customers. They give feedback to a company on improving product quality, planning, and monitoring its business schemes in order to increase sale and gain more profit. They are also helpful for customers to select the right products in less effort and time. Most companies make spam reviews of products in order to increase the products sales and gain more profit. Detecting spam product reviews is a challenging issue in NLP (natural language processing). Numerous machine learning approaches have attempted to detect and classify the product reviews as spam or nonspam. However, in order to improve the classification accuracy, this study has introduced an ensemble machine learning model that combines predictions from multilayer perceptron (MLP), K-Nearest Neighbour (KNN), and Random Forest (RF) and predicts the outcome of the review as spam or real (nonspam), based on the majority vote of the contributing models. In order to accomplish the task of spam review classification, the proposed ensemble and other benchmark boosting approaches are tested with 25 statistical features extracted from mobile application reviews of Yelp Dataset. Then, three different selection techniques are exploited to diminish the feature space and filter out the top 10 optimal features. The effectiveness of the proposed ensemble, the individual models, and other benchmark boosting approaches is again evaluated with 10 optimal features in terms of classification accuracy. Experimental outcomes illustrate that the proposed ensemble model outperformed the individual classifiers (MLP, KNN, and RF) and state-of-the-art boosting approaches like Generalized Boost Regression Model (GBM), Extreme Gradient Boost (XGBoost), and AdaBoost Regression Model in terms of classification accuracy.

Dyes are most commonly used in textile industries for colouring clothes. After colouring, dri-marine reactive red dye is drained into lakes and rivers, which is very hazardous for aquatic as well as human life. The treatment of this dye solution is necessary to make it clear before it is drained into river. For the treatment of this dye solution one of the cheapest and easy method is adsorption of dye with the natural adsorbents i.e. fuller׳s earth and rice husk. Data presented here focuses to improve the textural characteristics of both the adsorbents through chemical treatment. Selected chemicals for adsorbents treatment are acetic acid and sodium bicarbonate, both chemicals are very cheap, non-hazardous and never used before. Emphasis in this data article is to develop the easy and cost-effective method for removal of dri-marine reactive red dye. Keywords: Fuller׳s earth, Rice husk, Ultraviolet spectroscopy, Adsorption activation, Dri-marine reactive red, Surface morphology

Abstract The need of taxonomy is vital for knowledge sharing. This need has been portrayed by through-life engineering services/systems. This paper addresses this issue by repair process taxonomy development. Framework for repair process taxonomy was developed followed by its implementation. The importance of repair process taxonomy has been highlighted.

Abstract Background Despite advances in the gene-set enrichment analysis methods; inadequate definitions of gene-sets cause a major limitation in the discovery of novel biological processes from the transcriptomic datasets. Typically, gene-sets are obtained from publicly available pathway databases, which contain generalized definitions frequently derived by manual curation. Recently unsupervised clustering algorithms have been proposed to identify gene-sets from transcriptomics datasets deposited in public domain. These data-driven definitions of the gene-sets can be context-specific revealing novel biological mechanisms. However, the previously proposed algorithms for identification of data-driven gene-sets are based on hard clustering which do not allow overlap across clusters, a characteristic that is predominantly observed across biological pathways. Results We developed a pipeline using fuzzy-C-means (FCM) soft clustering approach to identify gene-sets which recapitulates topological characteristics of biological pathways. Specifically, we apply our pipeline to derive gene-sets from transcriptomic data measuring response of monocyte derived dendritic cells and A549 epithelial cells to influenza infections. Our approach apply Ward’s method for the selection of initial conditions, optimize parameters of FCM algorithm for human cell-specific transcriptomic data and identify robust gene-sets along with versatile viral responsive genes. Conclusion We validate our gene-sets and demonstrate that by identifying genes associated with multiple gene-sets, FCM clustering algorithm significantly improves interpretation of transcriptomic data facilitating investigation of novel biological processes by leveraging on transcriptomic data available in the public domain. We develop an interactive ‘Fuzzy Inference of Gene-sets (FIGS)’ package (GitHub: https://github.com/Thakar-Lab/FIGS ) to facilitate use of of pipeline. Future extension of FIGS across different immune cell-types will improve mechanistic investigation followed by high-throughput omics studies.

Waste water from the textile industry is one of the major sources of contamination causing serious health problems. In textile waste water, dyes are the main pollutant. In this research work textile waste water was treated with various organic acid-activated clays to remove the dyes. Pink Azo dye was selected as a component to remove from waste water. Adsorbent selected was bentonite clay and was activated by acids such as phosphoric, acetic, oxalic, and citric acids. Adsorbent characteristics have been analyzed through infrared spectroscopy (FTIR) and Scanning Electron Microscope (SEM) while the removal of dye at various temperatures has been analyzed through Ultraviolet (UV) analysis. It was observed that the bentonite clay-treated with phosphoric acid had relatively maximum capacity to remove the dye while the clay treated with oxalic acid got minimum capacity to remove the dye from same waste water.

The search for the genetic factors underlying complex neuropsychiatric disorders has proceeded apace in the past decade. Despite some advances in identifying genetic variants associated with psychiatric disorders, most variants have small individual contributions to risk. By contrast, disease risk increase appears to be less subtle for disease-predisposing environmental insults. In this study, we sought to identify associations between environmental pollution and risk of neuropsychiatric disorders. We present exploratory analyses of 2 independent, very large datasets: 151 million unique individuals, represented in a United States insurance claims dataset, and 1.4 million unique individuals documented in Danish national treatment registers. Environmental Protection Agency (EPA) county-level environmental quality indices (EQIs) in the US and individual-level exposure to air pollution in Denmark were used to assess the association between pollution exposure and the risk of neuropsychiatric disorders. These results show that air pollution is significantly associated with increased risk of psychiatric disorders. We hypothesize that pollutants affect the human brain via neuroinflammatory pathways that have also been shown to cause depression-like phenotypes in animal studies.

The development, implementation and advancement of technology solutions aimed at combating the COVID-19 outbreak are rapidly taking shape in India. Governments, Venture Capitalists, Academic Institutions, Incubators, Start-ups, and businesses large and small are all doing their part to deploy new innovative solutions as quickly as possible. Various databases were searched to look for different advancements in technology during the current coronavirus pandemic. It is seen that on one end nonpharmacological measure (social distancing, self-isolation, clean hands, and face masks) are time-tested and low-tech ways to help mitigate the viral spread. On the other end, Science and technology sector constituting of data science, machine learning, rapid diagnostic tests, mobile-first telehealth and computational simulation systems for drug development, artificial intelligence, virtual collaboration, and data tracking are complex ways of using the technology that have strengthened our pandemic response.

Accelerated partial breast irradiation (APBI) represents an evolving technique that is a standard of care option in appropriately selected woman following breast conserving surgery. While multiple techniques now exist to deliver APBI, interstitial brachytherapy represents the technique used in several randomized trials (National Institute of Oncology, GEC-ESTRO). More recently, many centers have adopted applicator-based brachytherapy to deliver APBI due to the technical complexities of interstitial brachytherapy. The purpose of this article is to review methods to evaluate and select patients for APBI, as well as to define potential workflow mechanisms that allow for the safe and effective delivery of APBI. Multiple consensus statements have been developed to guide clinicians on determining appropriate candidates for APBI. However, recent studies have demonstrated that these guidelines fail to stratify patients according to the risk of local recurrence, and updated guidelines are expected in the years to come. Critical elements of workflow to ensure safe and effective delivery of APBI include a multidisciplinary approach and evaluation, optimization of target coverage and adherence to normal tissue guideline constraints, and proper quality assurance methods.

Economic growth is a fundamental requirement for the development of a country. Therefore, the questions concerning the factors that can enhance the economic growth of a country are of immense importance especially for developing countries such as Pakistan. The present study provides evidence that promoting education and providing increased employment opportunities can produce positive return in the form of higher economic growth. It highlights the role that employment level and education can play in helping the economy to achieve rapid growth. To analyze the long-run and the short-run relationship among the key variables of interest, Johansen Co-integration test and Vector Error Correction Model (VECM) has been applied to annual time series data from 1980 to 2013. The empirical results confirm a positive long-run relationship between both employment and economic growth and education and economic growth.

Inguinal herniation of the urinary bladder is an extremely rare occurrence involving less than 5% of inguinal hernias reported in literature. These hernias require a high index of suspicion for their diagnosis and pose significant challenges to the operating surgeon. The majority of these hernias have been repaired by an open technique. We report two cases managed laparoscopically.

Introduction: There is a paucity of a standardized post-operative complications grading system in urology especially in the elderly population. Studies show satisfactory survival and oncological outcomes albeit with a slight increase in post-operative morbidity compared to younger patients. The Clavien-Dindo classification for post-operative complications is established as a valid system worldwide and applicable in many fields of surgery. Purpose: Retrospective assessment of post-operative complications in patients >75 years who underwent open/laparoscopic nephrectomy/nephroureterectomy for renal diseases and grading the post-operative complications according to the Clavien-Dindo classification. Materials and Methods: Retrospective review of case notes was performed in patients >75 years who underwent a laparoscopic/open nephrectomy/nephroureterectomy between 2000 and 2008. Post-operative complications were graded according to the Clavien-Dindo classification. Results: A total of 54 patients >75 years underwent nephrectomy/nephroureterectomy. 29 patients had laparoscopy and 25 had open surgery. Fifty one patients had a malignancy and 3 had benign diseases. Grade I, II, IIIa, IIIb and IVa were 25.6%, 41.1%, 7.7%, 7.7% and 17.9% respectively. No significant difference was noted in the 2 groups Conclusions: We believe that in elderly patients, laparoscopic surgery can be offered safely without significantly increasing the surgical risks. The Clavien-Dindo classification is easy to use and effectively applied to categorize post-operative complications associated with nephrectomy/nephroureterectomy in elderly population. However, this system needs slight modification to incorporate intra-operative complications and large studies are needed to validate and standardize this classification for all urological procedures.