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Fulltext Life table of Synthesiomyia nudiseta (Van der Wulp) (Diptera: Muscidae) under uncontrolled laboratory environments - a preliminary study

Aruna Devi A, Abu Hassan A, Kumara TK, Che Salmah MR

Trop Biomed, 2011 Dec;28(3):524-30.
PMID: 22433881

The life history of the male and female of the indoor forensic fly, Synthesiomyia nudiseta was studied under fluctuating temperature of indoor environments and analysed based on the age-stage and two sex life table. The life cycle of S. nudiseta was 14.0±1.0 days from the egg stage to adult emergence. The population parameters calculated were; net reproduction rate (R(o)= 108.6), mean generation time (T(o)= 12.2), intrinsic rate of increase (r(m)= 0.38), and finite rate of increase (λ= 1.46). The pre-oviposition period (APOP) was 6.0± 0.1 days. All population parameters suggested that S. nudiseta exhibits the r-strategist characteristics.
Performances of toluene removal by activated carbon derived from durian shell

Tham YJ, Latif PA, Abdullah AM, Shamala-Devi A, Taufiq-Yap YH

Bioresour Technol, 2011 Jan;102(2):724-8.
PMID: 20884200 DOI: 10.1016/j.biortech.2010.08.068

In the effort to find alternative low cost adsorbent for volatile organic vapors has prompted this research in assessing the effectiveness of activated carbon produced from durian shell in removing toluene vapors. Durian shells were impregnated with different concentrations of H3PO4 followed by carbonization at 500 °C for 20 min under nitrogen atmosphere. The prepared durian shell activated carbon (DSAC) was characterized for its physical and chemical properties. The removal efficiency of toluene by DSAC was performed using different toluene concentrations. Results showed that the highest BET surface area of the produced DSAC was 1404 m2/g. Highest removal efficiency of toluene vapors was achieved by using DSAC impregnated with 30% of acid concentration heated at 500 °C for 20 min heating duration. However, there is insignificant difference between removal efficiency of toluene by DSAC and different toluene concentrations. The toluene adsorption by DSAC was better fitted into Freundlich model.
Protein Profiling of Women with Spontaneous Preterm Birth

Immaculate Mbongo L, Yamunah Devi A, Zain S, Omar SZ, Mohamed Z

Pharmacology, 2015;96(1-2):44-8.
PMID: 26065725 DOI: 10.1159/000430857

Preterm birth (PTB) is the largest cause of neonatal mortality and morbidity in the world. Ethnicity disparity in the occurrence of PTB has been associated with the cytokine function. In this study, we aimed at examining cytokine levels in women with spontaneous preterm and term births.
Fulltext Conceptual interpretation and clinical applicability of a systematic review and meta-analysis about prognostic value of apolipoproteins in COVID-19 patients

Rajagopal M, Senthilnathan R, Shetty S, Devi A, Ravishankar Ram M, Samiappan S, et al.

Travel Med Infect Dis, 2022;46:102248.
PMID: 34990864 DOI: 10.1016/j.tmaid.2021.102248
Fulltext Deep learning techniques for automated Alzheimer's and mild cognitive impairment disease using EEG signals: A comprehensive review of the last decade (2013 - 2024)

Acharya M, Deo RC, Tao X, Barua PD, Devi A, Atmakuru A, et al.

Comput Methods Programs Biomed, 2025 Feb;259:108506.
PMID: 39581069 DOI: 10.1016/j.cmpb.2024.108506

BACKGROUND AND OBJECTIVES: Mild Cognitive Impairment (MCI) and Alzheimer's Disease (AD) are progressive neurological disorders that significantly impair the cognitive functions, memory, and daily activities. They affect millions of individuals worldwide, posing a significant challenge for its diagnosis and management, leading to detrimental impacts on patients' quality of lives and increased burden on caregivers. Hence, early detection of MCI and AD is crucial for timely intervention and effective disease management.
METHODS: This study presents a comprehensive systematic review focusing on the applications of deep learning in detecting MCI and AD using electroencephalogram (EEG) signals. Through a rigorous literature screening process based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, the research has investigated 74 different papers in detail to analyze the different approaches used to detect MCI and AD neurological disorders.
RESULTS: The findings of this study stand out as the first to deal with the classification of dual MCI and AD (MCI+AD) using EEG signals. This unique approach has enabled us to highlight the state-of-the-art high-performing models, specifically focusing on deep learning while examining their strengths and limitations in detecting the MCI, AD, and the MCI+AD comorbidity situations.
CONCLUSION: The present study has not only identified the current limitations in deep learning area for MCI and AD detection but also proposes specific future directions to address these neurological disorders by implement best practice deep learning approaches. Our main goal is to offer insights as references for future research encouraging the development of deep learning techniques in early detection and diagnosis of MCI and AD neurological disorders. By recommending the most effective deep learning tools, we have also provided a benchmark for future research, with clear implications for the practical use of these techniques in healthcare.
Lattice 123 pattern for automated Alzheimer's detection using EEG signal

Dogan S, Barua PD, Baygin M, Tuncer T, Tan RS, Ciaccio EJ, et al.

Cogn Neurodyn, 2024 Oct;18(5):2503-2519.
PMID: 39555305 DOI: 10.1007/s11571-024-10104-1

This paper presents an innovative feature engineering framework based on lattice structures for the automated identification of Alzheimer's disease (AD) using electroencephalogram (EEG) signals. Inspired by the Shannon information entropy theorem, we apply a probabilistic function to create the novel Lattice123 pattern, generating two directed graphs with minimum and maximum distance-based kernels. Using these graphs and three kernel functions (signum, upper ternary, and lower ternary), we generate six feature vectors for each input signal block to extract textural features. Multilevel discrete wavelet transform (MDWT) was used to generate low-level wavelet subbands. Our proposed model mirrors deep learning approaches, facilitating feature extraction in frequency and spatial domains at various levels. We used iterative neighborhood component analysis to select the most discriminative features from the extracted vectors. An iterative hard majority voting and a greedy algorithm were used to generate voted vectors to select the optimal channel-wise and overall results. Our proposed model yielded a classification accuracy of more than 98% and a geometric mean of more than 96%. Our proposed Lattice123 pattern, dynamic graph generation, and MDWT-based multilevel feature extraction can detect AD accurately as the proposed pattern can extract subtle changes from the EEG signal accurately. Our prototype is ready to be validated using a large and diverse database.
Fulltext Natural history of asymptomatic gallstones: differential behaviour in male and female subjects

Sood S, Winn T, Ibrahim S, Gobindram A, Arumugam AA, Razali NC, et al.

Med J Malaysia, 2015 Dec;70(6):341-5.
PMID: 26988206 MyJurnal

OBJECTIVE: The natural history of asymptomatic (silent) gallstones has been inadequately studied. Existing information derives from studies based on oral cholecystography or relatively small sample sizes. We planned a retrospective cohort study in subjects with gallstones to determine conversion rates from asymptomatic to symptomatic.
METHODS: We extracted data from computerised databases of one government hospital and two private clinics in Malaysia. Files were scrutinised to ensure that criteria for asymptomatic gallstones were fulfilled. Patients were called on telephone, further questioned to confirm that the gallstones at detection were truly asymptomatic, and asked about symptoms that were consistent with previously defined criteria for biliary colic. Appropriate ethical clearances were taken.
RESULTS: 213 (112 males) patients fulfilled the criteria for asymptomatic gallstones and could be contacted. 23 (10.8%) developed pain after an average follow up interval of 4.02 years (range 0.1-11 years). Conversion rates from asymptomatic to symptomatic gallstones were high in the first two years of follow up, averaging 4.03±0.965 per year. Over time the conversion rates slowed, and by year 10 the annual conversion rate averaged only 1.38±0.29. Conversion rates were much higher for females compared to males (F:M hazard ratio 3.23, SE 1.54, p>z 0.014). The lifetime risks for conversion approached 6.15% for males, and 22.1% for females.
CONCLUSION: In conclusion, asymptomatic gallstones are much more likely to convert to symptomatic in females than in males. Males in whom asymptomatic stones are discovered should be advised conservative treatment. Surgery may be preferable to conservative management if the subject is a young female.
m radiology records of Hospital
Study site: Computerised database, Hospital Selayang, Selangor; private clinics, Kuala Lumpur, Malaysia