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| 1 | Syzygium aromaticum ethanol extract reduces AlCl_3-induced neurotoxicity in mice brain through regulation of amyloid precursor protein and oxidative stress gene expression显示文摘Objective: To investigate the neuroprotective effects of Syzygium aromaticum(S.aromaticum)extract(500 mg/kg) on AlCl_3(300 mg/kg)-induced mouse model of oxidative stress and neurotoxicity.Methods: An ethanolic extract of S.aromaticum seeds was prepared and the active compounds were identified using nuclear magnetic resonance spectroscopy.BALB/c mice were divided into five groups(negative control, AlCl_3-treated, self-recovery, AlCl_3 + S.aromaticum, S.aromaticum only; n=10) and treated with AlCl_3 and S.aromaticum extract.Expression of oxidative markers [Superoxide dismutase 1(SOD1) and peroxiredoxin 6(Prdx6)] and amyloid precursor protein(APP) in the hippocampus and cortex was evaluated via PCR.Histopathological assessment was performed to investigate the extent of neurodegeneration.Results: It was observed that AlCl_3 exposure increased the expression of APP770 while simultaneously down regulated the expression of APP695.AlCl_3 also induced a significant decrease(P<0.05) and an increase(P<0.05) in the expression level of SOD1 and Prdx6, respectively.A substantial decrease substantial(P<0.05) in the density of Nissl substance was also observed in cortex of the mice treated with AlCl_3.Interestingly, treatment with S.aromaticum extract normalized the alterations in the expression level of SOD1, Prdx6 and APPisoforms and improved the neuronal structural damage.Conclusions: The results showed that S.aromaticum is a promising antioxidant and a neuroprotective agent. | Sanila Amber Syed Adnan Ali Shah Touqeer Ahmed Saadia Zahid | 2018 | Asian Pacific Journal of Tropical Medicine2018，11，2： | 0 |
| 2 | Multi-Modality and Feature Fusion-Based COVID-19 Detection Through Long Short-Term Memory显示文摘The Coronavirus Disease 2019(COVID-19)pandemic poses the worldwide challenges surpassing the boundaries of country,religion,race,and economy.The current benchmark method for the detection of COVID-19 is the reverse transcription polymerase chain reaction(RT-PCR)testing.Nevertheless,this testing method is accurate enough for the diagnosis of COVID-19.However,it is time-consuming,expensive,expert-dependent,and violates social distancing.In this paper,this research proposed an effective multimodality-based and feature fusion-based(MMFF)COVID-19 detection technique through deep neural networks.In multi-modality,we have utilized the cough samples,breathe samples and sound samples of healthy as well as COVID-19 patients from publicly available COSWARA dataset.Extensive set of experimental analyses were performed to evaluate the performance of our proposed approach.Several useful features were extracted from the aforementioned modalities that were then fed as an input to long short-term memory recurrent neural network algorithms for the classification purpose.Extensive set of experimental analyses were performed to evaluate the performance of our proposed approach.The experimental results showed that our proposed approach outperformed compared to four baseline approaches published recently.We believe that our proposed technique will assists potential users to diagnose the COVID-19 without the intervention of any expert in minimum amount of time. | Noureen Fatima Rashid Jahangir Ghulam Mujtaba Adnan Akhunzada Zahid Hussain Shaikh Faiza Qureshi | 2022 | Computers, Materials & Continua2022，，9： | 0 |
| 3 | A qualitative exploration of Pakistan’s street children, as a consequence of the poverty-disease cycle显示文摘Background:Street children are a global phenomenon,with an estimated population of around 150 million across the world.These children include those who work on the streets but retain their family contacts,and also those who practically live on the streets and have no or limited family contacts.In Pakistan,many children are forced to work on the streets due to health-related events occurring at home which require children to play a financially productive role from an early stage.An explanatory framework adapted from the poverty-disease cycle has been used to elaborate these findings.Methods:This study is a qualitative study,and involved 19 in-depth interviews and two key informant interviews,conducted in Rawalpindi,Pakistan,from February to May 2013.The data was audio taped and transcribed.Key themes were identified and built upon.The respondents were contacted through a gatekeeper ex-street child who was a member of the street children community.Results:We asked the children to describe their life stories.These stories led us to the finding that street children are always forced to attain altered social roles because health-related problems,poverty,and large family sizes leave them no choice but to enter the workforce and earn their way.We also gathered information regarding high-risk practices and increased risks of sexual and substance abuse,based on the street children’s increased exposure.These children face the issue of social exclusion because diseases and poverty push them into a life full of risks and hazards;a life which also confines their social role in the future.Conclusion:The street child community in Pakistan is on the rise.These children are excluded from mainstream society,and the absence of access to education and vocational skills reduces their future opportunities.Keeping in mind the implications of health-related events on these children,robust inter-sectoral interventions are required. | Muhammad Ahmed Abdullah Zeeshan Basharat Omairulhaq Lodhi Muhammad Hisham Khan Wazir Hameeda Tayyab Khan Nargis Yousaf Sattar Adnan Zahid | 2014 | Infectious Diseases of Poverty2014，3，1： | 0 |
| 4 | Machine learning enabled identification and real-time prediction of living plants’ stress using terahertz waves显示文摘Considering the ongoing climate transformations, the appropriate and reliable phenotyping information of plant leaves is quite significant for early detection of disease, yield improvement. In real-life digital agricultural environment, the real-time prediction and identification of living plants leaves has immensely grown in recent years. Hence, cost-effective and automated and timely detection of plans species is vital for sustainable agriculture. This paper presents a novel, non-invasive method aiming to establish a feasible, and viable technique for the precise identification and observation of altering behaviour of plants species at cellular level for four consecutive days by integrating machine learning (ML) and THz with a swissto12 materials characterization kit (MCK) in the frequency range of 0.75 to 1.1 THz. For this purpose, measurements observations data of seven various living plants leaves were determined and incorporate three different ML algorithms such as random forest (RF), support vector machine, (SVM), and K-nearest neighbour (KNN). The results demonstrated that RF exhibited higher accuracy of 98.87% followed by KNN and SVM with an accuracy of 94.64% and 89.67%, respectively, for precise detection of different leaves by observing their morphological features. In addition, RF outperformed other classifiers for determination of water-stressed leaves and having an accuracy of 99.42%. It is envisioned that proposed study can be proven beneficial and vital in digital agriculture technology for the timely detection of plants species to significantly help in mitigate yield and economic losses and improve crops quality. | Adnan Zahid Kia Dashtipour Hasan T.Abbas Ismail Ben Mabrouk Muath Al-Hasan Aifeng Ren Muhammad A.Imran Akram Alomainy Qammer H.Abbasi | 2022 | Defence Technology(防务技术)2022，18，8： | 0 |
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