Evaluation for Granulomatous Inflammation on Fine Needle Aspiration Cytology Using Special Stains

Background. Tuberculosis is the commonest infectious disease in the developing world. Many diagnostic tests are devised for its detection including direct smear examination. This study was designed to determine the frequency of cases positive for AFB and positive for fungus in patients diagnosed to have granulomatous inflammation on Fine Needle Aspiration Cytology using special stains. Materials and Methods. A descriptive cross-sectional survey was done on 100 cases of granulomatous inflammation consistent with tuberculosis diagnosed on fine needle aspiration cytology at the Department of Pathology, King Edward Medical University, Lahore. After reporting granulomatous inflammation on Hematoxylin & Eosin staining of aspirates from FNAC, some unstained slides were subjected to special stains, like ZN, GMS, and PAS. Cases positive for AFB on ZN stain and fungus on GMS/PAS were noted down along with their frequency and percentages. Results. Forty-four cases (44%) of AFB positive smears were reported in granulomatous inflammation while only 5% cases of fungus were reported down. Cervical lymph nodes were the most commonly involved site (87%), and females were affected more (62%) than males. Most cases of AFB-positive smears were associated with caseation necrosis (93%). Conclusion. Special stains should be done on all granulomatous inflammation cases seen on FNAC for confirmation of TB and ruling out other infectious causes

The development and validation of a scoring tool to predict the operative duration of elective laparoscopic cholecystectomy

Background: The ability to accurately predict operative duration has the potential to optimise theatre efficiency and utilisation, thus reducing costs and increasing staff and patient satisfaction. With laparoscopic cholecystectomy being one of the most commonly performed procedures worldwide, a tool to predict operative duration could be extremely beneficial to healthcare organisations. Methods: Data collected from the CholeS study on patients undergoing cholecystectomy in UK and Irish hospitals between 04/2014 and 05/2014 were used to study operative duration. A multivariable binary logistic regression model was produced in order to identify significant independent predictors of long (> 90 min) operations. The resulting model was converted to a risk score, which was subsequently validated on second cohort of patients using ROC curves. Results: After exclusions, data were available for 7227 patients in the derivation (CholeS) cohort. The median operative duration was 60 min (interquartile range 45–85), with 17.7% of operations lasting longer than 90 min. Ten factors were found to be significant independent predictors of operative durations > 90 min, including ASA, age, previous surgical admissions, BMI, gallbladder wall thickness and CBD diameter. A risk score was then produced from these factors, and applied to a cohort of 2405 patients from a tertiary centre for external validation. This returned an area under the ROC curve of 0.708 (SE = 0.013, p  90 min increasing more than eightfold from 5.1 to 41.8% in the extremes of the score. Conclusion: The scoring tool produced in this study was found to be significantly predictive of long operative durations on validation in an external cohort. As such, the tool may have the potential to enable organisations to better organise theatre lists and deliver greater efficiencies in care

Burnout among surgeons before and during the SARS-CoV-2 pandemic: an international survey

Background: SARS-CoV-2 pandemic has had many significant impacts within the surgical realm, and surgeons have been obligated to reconsider almost every aspect of daily clinical practice. Methods: This is a cross-sectional study reported in compliance with the CHERRIES guidelines and conducted through an online platform from June 14th to July 15th, 2020. The primary outcome was the burden of burnout during the pandemic indicated by the validated Shirom-Melamed Burnout Measure. Results: Nine hundred fifty-four surgeons completed the survey. The median length of practice was 10 years; 78.2% included were male with a median age of 37 years old, 39.5% were consultants, 68.9% were general surgeons, and 55.7% were affiliated with an academic institution. Overall, there was a significant increase in the mean burnout score during the pandemic; longer years of practice and older age were significantly associated with less burnout. There were significant reductions in the median number of outpatient visits, operated cases, on-call hours, emergency visits, and research work, so, 48.2% of respondents felt that the training resources were insufficient. The majority (81.3%) of respondents reported that their hospitals were included in the management of COVID-19, 66.5% felt their roles had been minimized; 41% were asked to assist in non-surgical medical practices, and 37.6% of respondents were included in COVID-19 management. Conclusions: There was a significant burnout among trainees. Almost all aspects of clinical and research activities were affected with a significant reduction in the volume of research, outpatient clinic visits, surgical procedures, on-call hours, and emergency cases hindering the training. Trial registration: The study was registered on clicaltrials.gov "NCT04433286" on 16/06/2020

Reliability Awareness Multiple Path Installation in Software Defined Networking using Machine Learning Algorithm

Link failure is still a severe problem in today's networking system. Transmission delays and data packet loss cause link failure in the network. Rapid connection recovery after a link breakdown is an important topic in networking. The failure of the networking link must be recovered whenever possible because it could cause blockage of network traffic and obstruct normal network operation. To overcome this difficulty, backup or secondary channels can be chosen adaptively and proactively in SDN based on data traffic dynamics in the network. When a network connection fails, packets must find a different way to their destination. The goal of this research is to find an alternative way. Our proposed methodology uses a machine-learning algorithm called Linear Regression to uncover alternative network paths. To provide for speedy failure recovery, the controller communicates this alternate path to the network switches ahead of time. We train, test, and validate the learning model using a machine learning approach. To simulate our proposed technique and locate the trials, we use the Mininet network simulator. The simulation results show that our suggested approach recovers link failure most effectively compared to existing solutions

Reliability Awareness Multiple Path Installation in Software Defined Networking using Machine Learning Algorithm

Link failure is still a severe problem in today's networking system. Transmission delays and data packet loss cause link failure in the network. Rapid connection recovery after a link breakdown is an important topic in networking. The failure of the networking link must be recovered whenever possible because it could cause blockage of network traffic and obstruct normal network operation. To overcome this difficulty, backup or secondary channels can be chosen adaptively and proactively in SDN based on data traffic dynamics in the network. When a network connection fails, packets must find a different way to their destination. The goal of this research is to find an alternative way. Our proposed methodology uses a machine-learning algorithm called Linear Regression to uncover alternative network paths. To provide for speedy failure recovery, the controller communicates this alternate path to the network switches ahead of time. We train, test, and validate the learning model using a machine learning approach. To simulate our proposed technique and locate the trials, we use the Mininet network simulator. The simulation results show that our suggested approach recovers link failure most effectively compared to existing solutions

Impact of Cropping Pattern and Climatic Parameters in Lower Chenab Canal System—Case Study from Punjab Pakistan

In Pakistan, groundwater resources are depleting at an alarming rate due to intensive pumping, shifting of cropping patterns, and climate change vulnerability. The present study is aimed at investigating groundwater stress in the command area of Lower Chenab Canal (LCC) and associated branch canals. Groundwater stress is determined by considering the cropping patterns, surface water availability, groundwater levels, climatic variation, and crop water requirement (CWR) in the LCC command area. The climatic data is obtained from the Pakistan Meteorological Department (PMD) from 1990 to 2020. The records of temporal variation in cropping patterns are obtained from the Crop Reporting Service (CRS), Directorate of Agriculture, Lahore for the 1995–2020 period and classified according to Rabi season (November to April) and Kharif season (May to October). The LCC surface water flows data and groundwater levels are collected from the Punjab Irrigation Department (PID) Lahore from 2003 to 2018 and from 1995 to 2016, respectively. The CWR is estimated using the Cropwat 8.0 model and groundwater levels are estimated using the Inverse Distance Weighted (IDW) tool of ArcGIS software. It has been determined that Faisalabad, Sheikhupura, and Toba Tek Singh are highly groundwater stress cities having an average drawdown rate of 0.58 m/year. The surface water availability is also decreased from 7.75 to 4.81 billion cubic meters (Bm3) for the Kharif season whilst 4.17 to 2.63 Bm3 for the Rabi season. This study concluded that due to severe conditions in highly stressed areas, policy planners, decision-makers, and stakeholders should sincerely take some steps for maintaining groundwater levels either by capacity building workshops for the farmers or limiting the number of tubewells

Impact of Cropping Pattern and Climatic Parameters in Lower Chenab Canal System - Case Study from Punjab Pakistan

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