688 research outputs found
Prognostic Properties of KRAS Gene Mutation Subtypes in Resected Pancreatic Cancer
Introduction Pancreatic ductal adenocarcinoma (PDAC) is an aggressive and therapy-resistant cancer with an overall 5-year survival rate of almost 12%, making it among the most lethal of all major cancers.1 PDAC has a distinct genomic profile, with somatic KRAS protooncogene mutations in ~90% of cases.2,3 Current literature has not reached a consensus on disease prognosis based on KRAS mutation subtype.2-5https://jdc.jefferson.edu/aoa_research_symposium_posters/1005/thumbnail.jp
Explainable AI in Diagnosing and Anticipating Leukemia Using Transfer Learning Method
This research paper focuses on Acute Lymphoblastic Leukemia (ALL), a form of
blood cancer prevalent in children and teenagers, characterized by the rapid
proliferation of immature white blood cells (WBCs). These atypical cells can
overwhelm healthy cells, leading to severe health consequences. Early and
accurate detection of ALL is vital for effective treatment and improving
survival rates. Traditional diagnostic methods are time-consuming, costly, and
prone to errors. The paper proposes an automated detection approach using
computer-aided diagnostic (CAD) models, leveraging deep learning techniques to
enhance the accuracy and efficiency of leukemia diagnosis. The study utilizes
various transfer learning models like ResNet101V2, VGG19, InceptionV3, and
InceptionResNetV2 for classifying ALL. The methodology includes using the Local
Interpretable Model-Agnostic Explanations (LIME) for ensuring the validity and
reliability of the AI system's predictions. This approach is critical for
overcoming the "black box" nature of AI, where decisions made by models are
often opaque and unaccountable. The paper highlights that the proposed method
using the InceptionV3 model achieved an impressive 98.38% accuracy,
outperforming other tested models. The results, verified by the LIME algorithm,
showcase the potential of this method in accurately identifying ALL, providing
a valuable tool for medical practitioners. The research underscores the impact
of explainable artificial intelligence (XAI) in medical diagnostics, paving the
way for more transparent and trustworthy AI applications in healthcare
The Prominence of Artificial Intelligence in COVID-19
In December 2019, a novel virus called COVID-19 had caused an enormous number
of causalities to date. The battle with the novel Coronavirus is baffling and
horrifying after the Spanish Flu 2019. While the front-line doctors and medical
researchers have made significant progress in controlling the spread of the
highly contiguous virus, technology has also proved its significance in the
battle. Moreover, Artificial Intelligence has been adopted in many medical
applications to diagnose many diseases, even baffling experienced doctors.
Therefore, this survey paper explores the methodologies proposed that can aid
doctors and researchers in early and inexpensive methods of diagnosis of the
disease. Most developing countries have difficulties carrying out tests using
the conventional manner, but a significant way can be adopted with Machine and
Deep Learning. On the other hand, the access to different types of medical
images has motivated the researchers. As a result, a mammoth number of
techniques are proposed. This paper first details the background knowledge of
the conventional methods in the Artificial Intelligence domain. Following that,
we gather the commonly used datasets and their use cases to date. In addition,
we also show the percentage of researchers adopting Machine Learning over Deep
Learning. Thus we provide a thorough analysis of this scenario. Lastly, in the
research challenges, we elaborate on the problems faced in COVID-19 research,
and we address the issues with our understanding to build a bright and healthy
environment.Comment: 63 pages, 3 tables, 17 figure
MOLECULAR CHARACTERIZATION OF MAJOR VECTOR MOSQUITOES OF BANGLADESH
Mosquito-borne diseases are considered major contributors to vector-borne diseases, threatening more than eighty per cent of the global population. Pest management depends on proper identification techniques. The barcode region of the cytochrome oxidase subunit I gene of mitochondrial DNA has recently been proposed as a systematic tool, functional in taxonomy and evolutionary study for species definition. This work is the first attempt to identify the main vector mosquito species from Bangladesh based on the MT-COI gene. Eleven vector mosquitos were identified. AT content (69%) was found to be higher than GC content (31%) at the COI barcode region of the mosquito. The interspecific genetic divergence range of medically important mosquitoes was 0.01-0.21. Haplotype analysis revealed that Mansonia annulifera diverged highly from its immediate ancestor by the highest mutational steps (59). Phylogenetic analysis indicated that species belonging to the same family were in the same major clade. Overall, our findings contribute to a better method of identifying major vector mosquito species by COI genes and for implementing management measures against mosquito pests in Bangladesh
Vashantor: A Large-scale Multilingual Benchmark Dataset for Automated Translation of Bangla Regional Dialects to Bangla Language
The Bangla linguistic variety is a fascinating mix of regional dialects that
adds to the cultural diversity of the Bangla-speaking community. Despite
extensive study into translating Bangla to English, English to Bangla, and
Banglish to Bangla in the past, there has been a noticeable gap in translating
Bangla regional dialects into standard Bangla. In this study, we set out to
fill this gap by creating a collection of 32,500 sentences, encompassing
Bangla, Banglish, and English, representing five regional Bangla dialects. Our
aim is to translate these regional dialects into standard Bangla and detect
regions accurately. To achieve this, we proposed models known as mT5 and
BanglaT5 for translating regional dialects into standard Bangla. Additionally,
we employed mBERT and Bangla-bert-base to determine the specific regions from
where these dialects originated. Our experimental results showed the highest
BLEU score of 69.06 for Mymensingh regional dialects and the lowest BLEU score
of 36.75 for Chittagong regional dialects. We also observed the lowest average
word error rate of 0.1548 for Mymensingh regional dialects and the highest of
0.3385 for Chittagong regional dialects. For region detection, we achieved an
accuracy of 85.86% for Bangla-bert-base and 84.36% for mBERT. This is the first
large-scale investigation of Bangla regional dialects to Bangla machine
translation. We believe our findings will not only pave the way for future work
on Bangla regional dialects to Bangla machine translation, but will also be
useful in solving similar language-related challenges in low-resource language
conditions
Development of Quantitative Rapid Isothermal Amplification Assay for Leishmania donovani
Quantification of pathogen load, although challenging, is of paramount importance for accurate diagnosis and clinical management of a range of infectious diseases in a point-of-need testing (PONT) scenario such as in resource-limited settings. We formulated a quantification approach to test the standard-curve based absolute quantification ability of isothermal recombinase polymerase amplification (RPA) assay. As a test of principle, a 10-fold dilution series of Leishmania donovani (LD) genomic DNA prepared in nuclease-free-water (NFW), and from culture-spiked-blood (CSB) were tested, and a 15 min assay was performed. A modified algorithm was formulated to derive the detection outcome. The threshold-record times (Tr) in seconds thus obtained were plotted against the initial load of parasite genomes for log-linear regression analysis. The quantitative RPA (Q-RPA) assay was further evaluated against a LD quantitative (q)-PCR assay with DNA extracted from visceral and post-Kala-azar dermal leishmaniasis case specimens and stratified into different ranges of threshold cycle (Ct). The best-fitted regression models were found linear with mean r2/root mean square error (RMSE) values of residual points (in seconds) estimated as 0.996/8.063 and 0.992/7.46 for replicated series of NFW and CSB, respectively. In both series, the lower limit of detection reached less than 0.1 parasite genome equivalent DNA. Absolute agreement between Q-RPA and LD-qPCR was found for test positivity, and strong positive correlations were observed between the Tr and Ct values (r = 0.89; p < 0.0001) as well as between the absolute parasite loads (r = 0.87; p < 0.0001) quantified by respective assays. The findings in this very first Q-RPA assay for leishmaniasis are suggestive of its potential in monitoring LD load in clinical specimens, and the development of rapid Q-RPA assays for other infectious diseases
Evaluation of Rapid Extraction Methods Coupled with a Recombinase Polymerase Amplification Assay for Point-of-Need Diagnosis of Post-Kala-Azar Dermal Leishmaniasis
To detect Post-kala-azar leishmaniasis (PKDL) cases, several molecular methods with promising diagnostic efficacy have been developed that involve complicated and expensive DNA extraction methods, thus limiting their application in resource-poor settings. As an alternative, we evaluated two rapid DNA extraction methods and determined their impact on the detection of the parasite DNA using our newly developed recombinase polymerase amplification (RPA) assay. Skin samples were collected from suspected PKDL cases following their diagnosis through national guidelines. The extracted DNA from three skin biopsy samples using three different extraction methods was subjected to RPA and qPCR. The qPCR and RPA assays exhibited highest sensitivities when reference DNA extraction method using Qiagen (Q) kit was followed. In contrast, the sensitivity of the RPA assay dropped to 76.7% and 63.3%, respectively, when the boil & spin (B&S) and SpeedXtract (SE) rapid extraction methods were performed. Despite this compromised sensitivity, the B&S-RPA technique yielded an excellent agreement with both Q-qPCR (k = 0.828) and Q-RPA (k = 0.831) techniques. As expected, the reference DNA extraction method was found to be superior in terms of diagnostic efficacy. Finally, to apply the rapid DNA extraction methods in resource-constrained settings, further methodological refinement is warranted to improve DNA yield and purity through rigorous experiments
Antidiarrheal activity of methanol extract of Piper sylvaticum (roxb.) stem in mice and in silico molecular docking of its isolated compounds
Background: Piper sylvaticum (Roxb.), is commonly used in traditional medicine to treat a number of disease like in asthma, diarrhea, chronic cough, cold, piles, tuberculosis, and wounds. In this study, we investigated the antidiarrheal activity of methanol extract of P. sylvaticum stem (Met.PSS) in animal models. Later, molecular docking study was performed to better understand its molecular mechanism and to determine the potent phytocompounds of this plant for the antidiarrheal property.Methods: The stems were extracted with methanol and subjected to in vivo antidiarrheal study using the castor oil-induced diarrhea and castor oil induced enteropooling tests in animal models. And then, in silico molecular docking study was performed using Schrödinger suite Maestro v10.1.Results: Met.PSS exhibited a dose-dependent and statistically significant antidiarrheal activity in both castor oil-induced diarrhea and enteropooling tests at the doses of 200 and 400 mg/kg. Additionally, our molecular docking analysis exhibited that four compounds viz. piperine, piperlonguminine, sylvamide, and sylvatine have the best binding affinity against the target enzyme (M3 muscarinic acetylcholine receptor) in comparison to reference drug Loperamide.Conclusions: The present study suggests that Met.PSS possess significant antidiarrheal activity which could be related to the presence of various secondary plant metabolites or phytochemicals. Additionally, the phytocompounds, i.e., piperine, piperlonguminine, sylvamide, and sylvatine were found to be most effective in molecular docking study
Evaluation of Loopamp™ Leishmania Detection Kit and Leishmania Antigen ELISA for Post-Elimination Detection and Management of Visceral Leishmaniasis in Bangladesh
With reduced prevalence of visceral leishmaniasis (VL) in the Indian subcontinent (ISC),
direct and field deployable diagnostic tests are needed to implement an effective
diagnostic and surveillance algorithm for post-elimination VL control. In this regard, here
we investigated the diagnostic efficacies of a loop-mediated isothermal amplification
(LAMP) assay (Loopamp™ Leishmania Detection Kit, Eiken Chemical CO., Ltd, Japan), a
real-time quantitative PCR assay (qPCR) and the Leishmania antigen ELISA (CLIN-TECH,
UK) with different sampling techniques and evaluated their prospect to incorporate into
post-elimination VL control strategies. Eighty clinically and rK39 rapid diagnostic test
confirmed VL cases and 80 endemic healthy controls were enrolled in the study.
Peripheral blood and dried blood spots (DBS) were collected from all the participants at
the time of diagnosis. DNA was extracted from whole blood (WB) and DBS via silica
columns (QIAGEN) and boil & spin (B&S) methods and tested with qPCR and Loopamp.
Urine was collected from all participants at the time of diagnosis and was directly
subjected to the Leishmania antigen ELISA. 41 patients were followed up and urine
samples were collected at day 30 and day 180 after treatment and ELISA was performed.
The sensitivities of the Loopamp-WB(B&S) and Loopamp-WB(QIA) were 96.2% (95% CI
89·43-99·22) and 95% (95% CI 87·69-98·62) respectively. The sensitivity of Loopamp-
DBS(QIA) was 85% (95% CI 75·26- 92·00). The sensitivities of the qPCR-WB(QIA) and
qPCR-DBS(QIA) were 93.8% (95% CI 86·01-97·94) and 72.5% (95% CI 61·38-81·90)
respectively. The specificity of all molecular assays was 100%. The sensitivity and
specificity of the Leishmania antigen ELISA were 97.5% (95% CI 91·47-99·70) and
91.95% (95% CI 84·12-96·70) respectively. The Leishmania antigen ELISA depicted
clinical cure at day 180 in all the followed-up cases. Efficacy and sustainability identify the
Loopamp-WB(B&S) and the Leishmania antigen ELISA as promising and minimally
invasive VL diagnostic tools to support VL diagnostic and surveillance activities
respectively in the post-elimination era
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