Statistical Pitfalls in Medical Research

In conducting and reporting of medical research, there are some common pitfalls in using statistical methodology which may result in invalid inferences being made. This paper is aimed to highlight to inexperienced statisticians or non-statistician some of the common statistical pitfalls encountered when using statistics to interpret data in medical research. We also comment on good practices to avoid these pitfalls. Malawi Medical Journal Vol. 20 (1) 2008 pp. 15-1

Application of DIGE and mass spectrometry in the study of type 2 Diabetes Mellitus (T2DM) mouse models

Knowledge of the differences between the amounts and types of protein that are expressed in diseased compared to healthy subjects may give an understanding of the biological pathways that cause disease. This is the reasoning behind the presented protocol, which uses difference gel electrophoresis to discover up‐ or down‐regulated proteins between mice of different genotypes, or of those fed on different diets, that may thus be prone to develop diabetes‐like phenotypes. Subsequent analysis of these proteins by tandem mass spectrometry typically facilitates their identification with a high degree of confidence

CISTO TRAUMÁTICO DA MANDÍBULA- RELATO DE CASO CLÍNICO

O  cisto  ósseo  traumático  é  uma  lesão  não  neoplásica  que representa aproximadamente 1% de todos os cistos maxilares, acometendo as regiões de corpo e sínfise de mandíbula com maior freqüência. Sua etiologia é desconhecida,  mas  acredita-se  que  o  trauma  local  seja  fator  relacionado  ao seu  desenvolvimento. Não  há  predileção  por  sexo  e  afeta mais  jovens  abaixo de 25 anos. O diagnóstico pode ser por radiografias de rotina que apresentam lesão radiolúcida, uniradicular, de crescimento lento, tamanho variável e limites definidos.  O  tratamento  é  cirúrgico  com  curetagem  e  punção

An Integrated Framework for the Quantification of Road Network Seismic Vulnerability and Accessibility to Critical Services

Road networks are regarded as the backbone of transportation systems, which play an important role in the social and economic prosperity of societies. Due to this reason, it is crucial to develop road networks with higher resiliency rates to operate normally during earthquake incidents. In the last decades, the research that tackled the management of disasters for road networks gained great attention, in particular by developing various seismic vulnerability assessment models. Most of those models study a single criterion, e.g., physical damage of road assets, traffic disruption, and/or functionality loss of the network without taking into consideration the combination of different vulnerability criteria. The proposed framework is part of the global seismic vulnerability assessment models that combine fragility functions and vulnerability indices, which is demonstrated by an application in a road network in the city of Penang in Malaysia. In the first step, the fragility functions are developed where their results are used to calculate the Seismic Vulnerability Index (SVI) for roadways by weighting the main investigated parameters. This is followed by investigating the Accessibility Index (AI) model that is employed to assess the accessibility of targeted districts within the investigated area. Subsequently, an integrated approach is employed to generate the emergency evacuation maps to critical service centres by referring to the correlations between vulnerability and the accessibility rates. In conclusion, the results of this study integrate engineering judgment and numerical models to create a comparative study for assessing the performance of road networks and to validate the significance of an integrated seismic assessment on various critical societal sectors, such as improving emergency accessibility and implementing better mitigation strategies for communities living in disaster-prone areas.Ministry of Higher Education (MOHE) through Fundamental Research Grant Scheme (FRGS/1/2020/TK02/USM/02/1)

DALC: Distributed Automatic LSTM Customization for Fine-Grained Traffic Speed Prediction

Over the past decade, several approaches have been introduced for short-term traffic prediction. However, providing fine-grained traffic prediction for large-scale transportation networks where numerous detectors are geographically deployed to collect traffic data is still an open issue. To address this issue, in this paper, we formulate the problem of customizing an LSTM model for a single detector into a finite Markov decision process and then introduce an Automatic LSTM Customization (ALC) algorithm to automatically customize an LSTM model for a single detector such that the corresponding prediction accuracy can be as satisfactory as possible and the time consumption can be as low as possible. Based on the ALC algorithm, we introduce a distributed approach called Distributed Automatic LSTM Customization (DALC) to customize an LSTM model for every detector in large-scale transportation networks. Our experiment demonstrates that the DALC provides higher prediction accuracy than several approaches provided by Apache Spark MLlib.Comment: 12 pages, 5 figures, the 34th International Conference on Advanced Information Networking and Applications (AINA 2020), Springe

Human Angiostrongyliasis Outbreak in Dali, China

Angiostrongyliasis, caused by the rat lungworm Angiostrongylus cantonensis, is a potentially fatal food-borne disease. It is endemic in parts of Southeast Asia, the Pacific Islands, Australia, and the Caribbean. Outbreaks have become increasingly common in China due to the spread of efficient intermediate host snails, most notably Pomacea canaliculata. However, infections are difficult to detect since the disease has a rather long incubation period and few diagnostic clinical symptoms. Reliable diagnostic tests are not widely available. The described angiostrongyliasis epidemic in Dali, China lasted for eight months. Only 11 of a total of 33 suspected patients were clinically confirmed based on a set of diagnostic criteria. Our results demonstrate that the rapid and correct diagnosis of the index patient is crucial to adequately respond to an epidemic, and a set of standardized diagnostic procedures is needed to guide clinicians. Integrated control and management measures including health education, clinical guidelines and a hospital-based surveillance system, should be implemented in areas where snails are a popular food item

Superconductivity at the Border of Electron Localization and Itinerancy

The superconducting state of iron pnictides and chalcogenides exists at the border of antiferromagnetic order. Consequently, these materials could provide clues about the relationship between magnetism and unconventional superconductivity. One explanation, motivated by the so-called bad-metal behaviour of these materials, proposes that magnetism and superconductivity develop out of quasi-localized magnetic moments which are generated by strong electron-electron correlations. Another suggests that these phenomena are the result of weakly interacting electron states that lie on nested Fermi surfaces. Here we address the issue by comparing the newly discovered alkaline iron selenide superconductors, which exhibit no Fermi-surface nesting, to their iron pnictide counterparts. We show that the strong-coupling approach leads to similar pairing amplitudes in these materials, despite their different Fermi surfaces. We also find that the pairing amplitudes are largest at the boundary between electronic localization and itinerancy, suggesting that new superconductors might be found in materials with similar characteristics.Comment: Version of the published manuscript prior to final journal-editting. Main text (23 pages, 4 figures) + Supplementary Information (14 pages, 7 figures, 3 tables). Calculation on the single-layer FeSe is added. Enhancement of the pairing amplitude in the vicinity of the Mott transition is highlighted. Published version is at http://www.nature.com/ncomms/2013/131115/ncomms3783/full/ncomms3783.htm