Evaluation of chemopreventive effect of Fumaria indica against N-nitrosodiethylamine and CCl4-induced hepatocellular carcinoma in Wistar rats

AbstractObjectiveTo investigation the chemopreventive potential of Fumaria indica (F. indica) extract (FIE) on N-nitrosodiethylamine and CCl4-induced hepatocarcinogenesis in Wistar rats.MethodsThe experimental animals were divided into six groups (n=6). Hepatocellular carcinoma was induced by single intraperitoneal injection of N-nitrosodiethylamine (NDEA) in normal saline at a dose of 200 mg/kg body weight followed by weekly subcutaneous injections of CCl4(3 mL/kg/week) for 6 weeks, as the promoter of carcinogenic effect. After administration of the carcinogen, 200 and 400 mg/kg of FIE were administered orally once a day throughout the study. At the end of 20 weeks, the body weight, liver weight and relative liver weight were measured. The percentage of nodule incidence and liver cancer markers such as aspartate transaminase (AST), alanine transaminase (ALT), alkaline phosphatase (ALP), γ-glutamyl transferase (γ-GT), total bilirubin level (TBL), α-feto protein (AFP) and carcinoembryonic antigen were estimated along with histopathological investigation in experimental groups of rats.ResultsObtained results demonstrated that the cotreatment with FIE significantly prevented the decrease of the body weight and also increased in relative liver weight caused by NDEA. The treatment with FIE significantly reduced the nodule incidence and nodule multiplicity in the rats after NDEA administration. The levels of liver cancer markers such as AST, ALT, ALP, γ-glutamyl transferase, TBL, AFP and carcinoembryonic antigen were substantially increased by NDEA treatment. However, FIE treatment significantly reduced the liver injury and restored the entire liver cancer markers. Histological observations of liver tissues too correlated with the biochemical observations.ConclusionsThese finding powerfully supports that F. indica exert chemopreventive effect by suppressing the tumor burden and restoring the activities of hepatic cancer marker enzymes on NDEA and CCl4-induced hepatocarcinogenesis in Wistar rats

Geospatial techniques for comparative case study of spatiotemporal changes in New Karachi and North Karachi parks

The well-known fact is that parks play a significant role in sustaining the urban environment. Megacities like Karachi are developing rapidly with a simultaneous increase in the city area, putting immense pressure on open green spaces. The widespread built-up development is replacing the previously existing vegetative cover. The lack of green spaces is the main concern, and this problem will only worsen due to overpopulation associated with the rapid growth of cities. The lack of evidence-based planning contributes to the unbalanced spatial distribution of parks in quantity and quality. The present research aimed to compare and find out the quality and status of parks such as park areas under encroachment and temporal changes in the vegetative cover of parks in the predominantly low to middle-income residential areas of New Karachi and North Karachi Towns of Karachi metropolitan. Geospatial techniques have been used for mapping, assessments, and analyses. Results indicate that boundary walls are a good solution to stop or reduce park encroachments as correlation indicates the parks with boundary walls have a significantly lower percentage of encroachment in 2022. The existing work indicated that the number of trees has increased in most parks in both towns in 2022. The overall correlation results indicate that factors affecting park quality positively have a positive association with other positively affecting factors and a negative association with factors that affect park quality negatively. There is a dire need to implement better planning strategies to enhance the quality of existing parks and construct new parks in the study area

Land Use Analysis of Central Business District (CBD) of Metropolis Saddar Karachi through SRS/GIS Techniques

The high density of buildings and roads are commonly associated with the Central Business District (CBD) of a metropolitan and multicultural city Karachi, Pakistan. It is the highly interactive place of a metropolis, therefore, considers functionally effective zones. This paper will prove even with a high rate of urbanization and expansion due to urban sprawl, still Saddar is the focus of attraction concerning several facts. The main objectives of the study were to explore the land-based cataloging of Saddar based on activity and to assess the environmental issues which are associated with this land use classification for the sustainability of CBD the people perception methods of identification of research, Land-use (LU) Analysis of Area of Interest (AOI) via, questionnaire-based surveying, and geo-coding of activities methods have been used in this study.  The obtained results revealed that Saddar town covers land use approximately, 4.28% Leisure, 9.38% Shopping, Business or Trade, 7.9% Social, Institutional, or Infrastructure Related, 4.62% Mass Assembly of People, 6.37% Industrial, Manufacturing, and Waste Related, while 5.68% Traveler Movement,5.9% Natural Resource Related,52.40% Residential,3.4% No Human Activity or Unclassified. Approximately, 44.2% of the land use was engaged in capita producing activities, which reflects CBD’s functional strength.  Overall, it recommended that there should be more green spaces in the CBD to improve air quality. Vertical urban gardening/forest can be implemented as Saddar has limited space and it is a concrete jungle having very less open space

Person Identification through Harvesting Kinetic Energy

Energy-based devices made this possible to recognize the need for batteryless wearables. The batteryless wearable notion created an opportunity for continuous and ubiquitous human identification. Traditionally, securing device passwords, PINs, and fingerprints based on the accelerometer to sample the acceleration traces for identification, but the accelerometer's energy consumption has been a critical issue for the existing ubiquitous self-enabled devices. In this paper, a novel method harvesting kinetic energy for identification improves energy efficiency and reduces energy demand to provide the identification. The idea of utilizing harvested power for personal identification is actuated by the phenomena that people walk distinctly and generate different kinetic energy levels leaving their signs with a harvested power signal. The statistical evaluation of experimental results proves that power traces contain sufficient information for person identification. The experimental analysis is conducted on 85 persons walking data for kinetic power signal-based person identification. We select five different classifiers that provide exemplary performance for identifying an individual for their generated power traces, namely NaiveBayes, OneR, and Meta Bagging. The experimental outcomes demonstrate the classifier's accuracy of 90%, 97%, and 98%, respectively. The Dataset used is publicly available for the gait acceleration series

Pitfalls in machine learning‐based assessment of tumor‐infiltrating lymphocytes in breast cancer: a report of the international immuno‐oncology biomarker working group

The clinical significance of the tumor-immune interaction in breast cancer (BC) has been well established, and tumor-infiltrating lymphocytes (TILs) have emerged as a predictive and prognostic biomarker for patients with triple-negative (estrogen receptor, progesterone receptor, and HER2 negative) breast cancer (TNBC) and HER2-positive breast cancer. How computational assessment of TILs can complement manual TIL-assessment in trial- and daily practices is currently debated and still unclear. Recent efforts to use machine learning (ML) for the automated evaluation of TILs show promising results. We review state-of-the-art approaches and identify pitfalls and challenges by studying the root cause of ML discordances in comparison to manual TILs quantification. We categorize our findings into four main topics; (i) technical slide issues, (ii) ML and image analysis aspects, (iii) data challenges, and (iv) validation issues. The main reason for discordant assessments is the inclusion of false-positive areas or cells identified by performance on certain tissue patterns, or design choices in the computational implementation. To aid the adoption of ML in TILs assessment, we provide an in-depth discussion of ML and image analysis including validation issues that need to be considered before reliable computational reporting of TILs can be incorporated into the trial- and routine clinical management of patients with TNBC

Effect of angiotensin-converting enzyme inhibitor and angiotensin receptor blocker initiation on organ support-free days in patients hospitalized with COVID-19

IMPORTANCE Overactivation of the renin-angiotensin system (RAS) may contribute to poor clinical outcomes in patients with COVID-19. Objective To determine whether angiotensin-converting enzyme (ACE) inhibitor or angiotensin receptor blocker (ARB) initiation improves outcomes in patients hospitalized for COVID-19. DESIGN, SETTING, AND PARTICIPANTS In an ongoing, adaptive platform randomized clinical trial, 721 critically ill and 58 non–critically ill hospitalized adults were randomized to receive an RAS inhibitor or control between March 16, 2021, and February 25, 2022, at 69 sites in 7 countries (final follow-up on June 1, 2022). INTERVENTIONS Patients were randomized to receive open-label initiation of an ACE inhibitor (n = 257), ARB (n = 248), ARB in combination with DMX-200 (a chemokine receptor-2 inhibitor; n = 10), or no RAS inhibitor (control; n = 264) for up to 10 days. MAIN OUTCOMES AND MEASURES The primary outcome was organ support–free days, a composite of hospital survival and days alive without cardiovascular or respiratory organ support through 21 days. The primary analysis was a bayesian cumulative logistic model. Odds ratios (ORs) greater than 1 represent improved outcomes. RESULTS On February 25, 2022, enrollment was discontinued due to safety concerns. Among 679 critically ill patients with available primary outcome data, the median age was 56 years and 239 participants (35.2%) were women. Median (IQR) organ support–free days among critically ill patients was 10 (–1 to 16) in the ACE inhibitor group (n = 231), 8 (–1 to 17) in the ARB group (n = 217), and 12 (0 to 17) in the control group (n = 231) (median adjusted odds ratios of 0.77 [95% bayesian credible interval, 0.58-1.06] for improvement for ACE inhibitor and 0.76 [95% credible interval, 0.56-1.05] for ARB compared with control). The posterior probabilities that ACE inhibitors and ARBs worsened organ support–free days compared with control were 94.9% and 95.4%, respectively. Hospital survival occurred in 166 of 231 critically ill participants (71.9%) in the ACE inhibitor group, 152 of 217 (70.0%) in the ARB group, and 182 of 231 (78.8%) in the control group (posterior probabilities that ACE inhibitor and ARB worsened hospital survival compared with control were 95.3% and 98.1%, respectively). CONCLUSIONS AND RELEVANCE In this trial, among critically ill adults with COVID-19, initiation of an ACE inhibitor or ARB did not improve, and likely worsened, clinical outcomes. TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT0273570

Gentle Slow Start to Alleviate TCP Incast in Data Center Networks

Modern data center networks typically adopt symmetric topologies, such as leaf-spine and fat-tree. When a large number of transmission control protocol (TCP) flows in data center networks send data to the same receiver, the congestion collapse, called TCP Incast, frequently happens because of the huge packet losses and Time-Out. To address the TCP Incast issue, we firstly demonstrate that adjusting the increasing speed of the congestion window during the slow start phase is crucially important. Then we propose the Gentle Slow Start (GSS) algorithm, which adjusts the congestion window according to real-time congestion state in a gentle manner and smoothly switches from slow start to congestion avoidance phase. Furthermore, we present the implementation and design of Gentle Slow Start and also integrate it into the state-of-the-art data center transport protocols. The test results show that GSS effectively decreases the Incast probability and increases the network goodput by average 8x

Vehicular Ad Hoc Network (VANET) Connectivity Analysis of a Highway Toll Plaza

The aim of this paper was to study issues of network connectivity in vehicular ad hoc networks (VANETs) to avoid traffic congestion at a toll plaza. An analytical model was developed for highway scenarios where the traffic congestion could have the vehicles reduce their speed instead of blocking the flow of traffic. In this model, nearby vehicles must be informed when traffic congestion occurs before reaching the toll plaza so they can reduce their speed in order to avoid traffic congestion. Once they have crossed the toll plaza they can travel on at their normal speed. The road was divided into two or three sub-segments to help analyze the performance of connectivity. The proposed analytical model considered various parameters that might disturb the connectivity probability, including traveling speed, communication range of vehicles, vehicle arrival rate, and road length. The simulation results matched those of the analytical model, which showed the analytical model developed in this paper is effective