Marsupialization of a large dentigerous cyst in the mandible with orthodontic extrusion of three impacted teeth. A case report

The dentigerous cyst (DC) is the most common type of developmental odontogenic cyst. It is usually asymptomatic and associated with the crown of an unerupted or impacted tooth. However, after a long duration, it is likely to cause significant bone resorption, cortical expansion, and tooth displacement. This report presents a large infected DC in the mandible of a 12-year-old female patient. The DC was located inferior to badly decayed primary molars and surrounded three impacted permanent teeth: canine, first premolar, which had a dilacerated root, and second premolar. The DC was treated successfully by marsupialization and extrusion of the impacted teeth. In conclusion, the combination of marsupialization with orthodontic extrusion is a conservative, efficient protocol that stimulates bone healing and promotes the eruption of cyst-associated teeth even if they are deeply impacted, crowded, or have a dilacerated root

Issues in performance evaluation for host–pathogen protein interaction prediction

The study of interactions between host and pathogen proteins is important for understanding the underlying mechanisms of infectious diseases and for developing novel therapeutic solutions. Wet-lab techniques for detecting protein–protein interactions (PPIs) can benefit from computational predictions. Machine learning is one of the computational approaches that can assist biologists by predicting promising PPIs. A number of machine learning based methods for predicting host–pathogen interactions (HPI) have been proposed in the literature. The techniques used for assessing the accuracy of such predictors are of critical importance in this domain. In this paper, we question the effectiveness of K-fold cross-validation for estimating the generalization ability of HPI prediction for proteins with no known interactions. K-fold cross-validation does not model this scenario, and we demonstrate a sizable difference between its performance and the performance of an alternative evaluation scheme called leave one pathogen protein out (LOPO) cross-validation. LOPO is more effective in modeling the real world use of HPI predictors, specifically for cases in which no information about the interacting partners of a pathogen protein is available during training. We also point out that currently used metrics such as areas under the precision-recall or receiver operating characteristic curves are not intuitive to biologists and propose simpler and more directly interpretable metrics for this purpose

Financial Development and Output Volatility Nexus: The Role of Financial Sector Instability

This research study explores the influence of financial sector development on output volatility. Particularly, the role of financial sector instability is modeled to provide a better understating of financial sector development and output volatility nexus. The empirical analysis is based on cross-sectional panel datasets for 180 countries from 1971 through 2020. In addition to Random and Fixed Effects models, the 2-SLS and GMM techniques were used for empirical analysis. Country analyses produce mixed results but show a considerable beneficial result. The results suggest that financial sector volatility increases output volatility. While financial development, on the other hand, is critical in protecting output from instability. Trade openness and inflation have also been controlled for their impact on output volatility. Trade openness, like financial stability, decreases production volatility. Inflation, as a monetary phenomenon, tends to amplify output volatility

SPREAD OF COVID-19 AND ITS IMPACT ON PAKISTAN: A NEED FOR PROSPECTIVE PLANNING

COVID-19, a global pandemic, has spread in about 215 countries of the world. It has caused enormous damage to the lives, economies, and socio-political fabric of the societies. More than 14 million people stand affected world-wide, with death toll crossing 0.75 million. The expert virologists apprehend that the worst situation is yet not over. The IMF, World Bank, and WTO have issued warnings about the impending global recession. It has also been reported that low-income countries and weak segments of the societies have been the major victims of this malady. Keeping in view the fragile economy and unsound healthcare system of Pakistan, WHO and experts of various fields have predicted multiple effects on various aspects of our national life. The paper in hand attempts to analyze the magnitude of the spread of COVID-19 in Pakistan and its implications for our national security, with a view to offering policy recommendations.   Bibliography Entry Abbasi, Manzoor Ahmed, Muhammad Arshad, and Raja Nadir Mahmood Khan. 2020. "Spread of Covid-19 and Its Impact on Pakistan: A Need for Prospective Planning." Margalla Papers 24 (1): 16-30

The role and potential of soft power in mitigating the rivalry between India and Pakistan

The relations between India and Pakistan are currently stalemated owing to ongoing tensions. These tensions are rooted in long-term rivalry between the two nations. Soft power is one area where there are unifying elements in both states. This paper argues that the interaction in the domain of soft power such as sports, film industry and literary events between the two states can mitigate the rivalry. Due to unresolved conflicts people in both countries view competitions in sports as a matter of war. Soft power can change this perception at popular level which can be a crucial factor for resolving conflict and usher in an era of durable peace between the two countries.http://pu.edu.pk/home/journal/14am2022Political Science

CaMELS : In silicoprediction of calmodulin binding proteins and their binding sites

Due to Ca2+‐dependent binding and the sequence diversity of Calmodulin (CaM) binding proteins, identifying CaM interactions and binding sites in the wet‐lab is tedious and costly. Therefore, computational methods for this purpose are crucial to the design of such wet‐lab experiments. We present an algorithm suite called CaMELS (CalModulin intEraction Learning System) for predicting proteins that interact with CaM as well as their binding sites using sequence information alone. CaMELS offers state of the art accuracy for both CaM interaction and binding site prediction and can aid biologists in studying CaM binding proteins. For CaM interaction prediction, CaMELS uses protein sequence features coupled with a large‐margin classifier. CaMELS models the binding site prediction problem using multiple instance machine learning with a custom optimization algorithm which allows more effective learning over imprecisely annotated CaM‐binding sites during training. CaMELS has been extensively benchmarked using a variety of data sets, mutagenic studies, proteome‐wide Gene Ontology enrichment analyses and protein structures. Our experiments indicate that CaMELS outperforms simple motif‐based search and other existing methods for interaction and binding site prediction. We have also found that the whole sequence of a protein, rather than just its binding site, is important for predicting its interaction with CaM. Using the machine learning model in CaMELS, we have identified important features of protein sequences for CaM interaction prediction as well as characteristic amino acid sub‐sequences and their relative position for identifying CaM binding sites. Python code for training and evaluating CaMELS together with a webserver implementation is available at the URL: http://faculty.pieas.edu.pk/fayyaz/software.html#camels

Optimization of extraction of polyphenols from Sorghum Moench using response surface methodology, and determination of their antioxidant activities

Purpose: To employ response surface methodology (RSM) hinged on a central composite design (CCD) for the optimization of the extraction of polyphenols from Sorghum moench (Sorghum M).Methods: The combined influence of independent variables were assessed with RSM. Total phenolic content (TPC) determination was carried out using Folin-Ciocalteu method. Derivative compounds of phenolic acid were assayed using high performance liquid (HPLC). Antioxidant potential was determined through 1,1-diphenyl-2- picrylhydrazyl (DPPH) radical scavenging test.Results: The optimized extraction conditions were: 60.37 % ethanol, temperature of 59.07 oC and 2.97 h of extraction duration, which resulted in the extraction of maximum amount of TPC, i.e., 313 mg GAE/100g dry weight. The interactions between temperature and ethanol concentration, and between extraction time and ethanol concentration had significant effects of TPC (p < 0.05). Under these conditions, there was a consistency between the projected and actual experimental levels of polyphenols. A positive correlation was found between TPC and DPPH radical scavenging activity (r=0.67, p <0.05). Furthermore, ferulic acid correlated positively with p-coumaric acid (r = 0.54, p <0.01).Conclusion: These results underscore the usefulness of conditions for extraction in accuratequantification of antioxidants and phenolic compounds from Sorghum M, for possible application in large scale commercial extraction.Keywords: Response surface methodology, Sorghum moench, Polyphenols, Antioxidant