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Public Health Public Health

Demographic characteristics, clinical symptoms, biochemical markers and probability of occurrence of severe dengue: A multicenter hospital-based study in Bangladesh.

In PLoS neglected tropical diseases ; h5-index 79.0

Establishing reliable early warning models for severe dengue cases is a high priority to facilitate triage in dengue-endemic areas and optimal use of limited resources. However, few studies have identified the complex interactive relationship between potential risk factors and severe dengue. This research aimed to assess the potential risk factors and detect their high-order combinative effects on severe dengue. A structured questionnaire was used to collect detailed dengue outbreak data from eight representative hospitals in Dhaka, Bangladesh, in 2019. Logistic regression and machine learning models were used to examine the complex effects of demographic characteristics, clinical symptoms, and biochemical markers on severe dengue. A total of 1,090 dengue cases (158 severe and 932 non-severe) were included in this study. Dyspnoea (Odds Ratio [OR] = 2.87, 95% Confidence Interval [CI]: 1.72 to 4.77), plasma leakage (OR = 3.61, 95% CI: 2.12 to 6.15), and hemorrhage (OR = 2.33, 95% CI: 1.46 to 3.73) were positively and significantly associated with the occurrence of severe dengue. Classification and regression tree models showed that the probability of occurrence of severe dengue cases ranged from 7% (age >12.5 years without plasma leakage) to 92.9% (age ≤12.5 years with dyspnoea and plasma leakage). The random forest model indicated that age was the most important factor in predicting severe dengue, followed by education, plasma leakage, platelet, and dyspnoea. The research provides new evidence to identify key risk factors contributing to severe dengue cases, which could be beneficial to clinical doctors to identify and predict the severity of dengue early.

Yang Jingli, Mosabbir Abdullah Al, Raheem Enayetur, Hu Wenbiao, Hossain Mohammad Sorowar

2023-Mar-15

General General

A combinatorial approach to uncover an additional Integrator subunit.

In Cell reports ; h5-index 119.0

RNA polymerase II (RNAPII) controls expression of all protein-coding genes and most noncoding loci in higher eukaryotes. Calibrating RNAPII activity requires an assortment of polymerase-associated factors that are recruited at sites of active transcription. The Integrator complex is one of the most elusive transcriptional regulators in metazoans, deemed to be recruited after initiation to help establish and modulate paused RNAPII. Integrator is known to be composed of 14 subunits that assemble and operate in a modular fashion. We employed proteomics and machine-learning structure prediction (AlphaFold2) to identify an additional Integrator subunit, INTS15. We report that INTS15 assembles primarily with the INTS13/14/10 module and interfaces with the Int-PP2A module. Functional genomics analysis further reveals a role for INTS15 in modulating RNAPII pausing at a subset of genes. Our study shows that omics approaches combined with AlphaFold2-based predictions provide additional insights into the molecular architecture of large and dynamic multiprotein complexes.

Offley Sarah R, Pfleiderer Moritz M, Zucco Avery, Fraudeau Angelique, Welsh Sarah A, Razew Michal, Galej Wojciech P, Gardini Alessandro

2023-Mar-13

AlphaFold2, CP: Neuroscience, Integrator complex, RNA polymerase II, RNA polymerase II pausing, large protein complexes, molecular modeling, omics, pause-release, transcription, transcription factors, transcriptional regulation

General General

Development and validation of deep learning based embryo selection across multiple days of transfer.

In Scientific reports ; h5-index 158.0

This work describes the development and validation of a fully automated deep learning model, iDAScore v2.0, for the evaluation of human embryos incubated for 2, 3, and 5 or more days. We trained and evaluated the model on an extensive and diverse dataset including 181,428 embryos from 22 IVF clinics across the world. To discriminate the transferred embryos with known outcome, we show areas under the receiver operating curve ranging from 0.621 to 0.707 depending on the day of transfer. Predictive performance increased over time and showed a strong correlation with morphokinetic parameters. The model's performance is equivalent to the KIDScore D3 model on day 3 embryos while it significantly surpasses the performance of KIDScore D5 v3 on day 5+ embryos. This model provides an analysis of time-lapse sequences without the need for user input, and provides a reliable method for ranking embryos for their likelihood of implantation, at both cleavage and blastocyst stages. This greatly improves embryo grading consistency and saves time compared to traditional embryo evaluation methods.

Theilgaard Lassen Jacob, Fly Kragh Mikkel, Rimestad Jens, Nygård Johansen Martin, Berntsen Jørgen

2023-Mar-14

oncology Oncology

Investigation of liquid biopsy analytes in peripheral blood of individuals after SARS-CoV-2 infection.

In EBioMedicine

BACKGROUND : Post-acute COVID-19 syndrome (PACS) is linked to severe organ damage. The identification and stratification of at-risk SARS-CoV-2 infected individuals is vital to providing appropriate care. This exploratory study looks for a potential liquid biopsy signal for PACS using both manual and machine learning approaches.

METHODS : Using a high definition single cell assay (HDSCA) workflow for liquid biopsy, we analysed 100 Post-COVID patients and 19 pre-pandemic normal donor (ND) controls. Within our patient cohort, 73 had received at least 1 dose of vaccination prior to SARS-CoV-2 infection. We stratified the COVID patients into 25 asymptomatic, 22 symptomatic COVID-19 but not suspected for PACS and 53 PACS suspected. All COVID-19 patients investigated in this study were diagnosed between April 2020 and January 2022 with a median 243 days (range 16-669) from diagnosis to their blood draw. We did a histopathological examination of rare events in the peripheral blood and used a machine learning model to evaluate predictors of PACS.

FINDINGS : The manual classification found rare cellular and acellular events consistent with features of endothelial cells and platelet structures in the PACS-suspected cohort. The three categories encompassing the hypothesised events were observed at a significantly higher incidence in the PACS-suspected cohort compared to the ND (p-value < 0.05). The machine learning classifier performed well when separating the NDs from Post-COVID with an accuracy of 90.1%, but poorly when separating the patients suspected and not suspected of PACS with an accuracy of 58.7%.

INTERPRETATION : Both the manual and the machine learning model found differences in the Post-COVID cohort and the NDs, suggesting the existence of a liquid biopsy signal after active SARS-CoV-2 infection. More research is needed to stratify PACS and its subsyndromes.

FUNDING : This work was funded in whole or in part by Fulgent Genetics, Kathy and Richard Leventhal and Vassiliadis Research Fund. This work was also supported by the National Cancer InstituteU54CA260591.

Qi Elizabeth, Courcoubetis George, Liljegren Emmett, Herrera Ergueen, Nguyen Nathalie, Nadri Maimoona, Ghandehari Sara, Kazemian Elham, Reckamp Karen L, Merin Noah M, Merchant Akil, Mason Jeremy, Figueiredo Jane C, Shishido Stephanie N, Kuhn Peter

2023-Mar-13

COVID-19, Liquid biopsy, Long COVID, Post-COVID sequelae, Post-acute COVID-19 syndrome (PACS), SARS-CoV-2

General General

Plant Disease Detection using Region-Based Convolutional Neural Network

ArXiv Preprint

Agriculture plays an important role in the food and economy of Bangladesh. The rapid growth of population over the years also has increased the demand for food production. One of the major reasons behind low crop production is numerous bacteria, virus and fungal plant diseases. Early detection of plant diseases and proper usage of pesticides and fertilizers are vital for preventing the diseases and boost the yield. Most of the farmers use generalized pesticides and fertilizers in the entire fields without specifically knowing the condition of the plants. Thus the production cost oftentimes increases, and, not only that, sometimes this becomes detrimental to the yield. Deep Learning models are found to be very effective to automatically detect plant diseases from images of plants, thereby reducing the need for human specialists. This paper aims at building a lightweight deep learning model for predicting leaf disease in tomato plants. By modifying the region-based convolutional neural network, we design an efficient and effective model that demonstrates satisfactory empirical performance on a benchmark dataset. Our proposed model can easily be deployed in a larger system where drones take images of leaves and these images will be fed into our model to know the health condition.

Hasin Rehana, Muhammad Ibrahim, Md. Haider Ali

2023-03-16

oncology Oncology

Geometric and dosimetric evaluation of deep learning based auto-segmentation for clinical target volume on breast cancer.

In Journal of applied clinical medical physics ; h5-index 28.0

BACKGROUND : Recently, target auto-segmentation techniques based on deep learning (DL) have shown promising results. However, inaccurate target delineation will directly affect the treatment planning dose distribution and the effect of subsequent radiotherapy work. Evaluation based on geometric metrics alone may not be sufficient for target delineation accuracy assessment. The purpose of this paper is to validate the performance of automatic segmentation with dosimetric metrics and try to construct new evaluation geometric metrics to comprehensively understand the dose-response relationship from the perspective of clinical application.

MATERIALS AND METHODS : A DL-based target segmentation model was developed by using 186 manual delineation modified radical mastectomy breast cancer cases. The resulting DL model were used to generate alternative target contours in a new set of 48 patients. The Auto-plan was reoptimized to ensure the same optimized parameters as the reference Manual-plan. To assess the dosimetric impact of target auto-segmentation, not only common geometric metrics but also new spatial parameters with distance and relative volume ( R V ${R}_V$ ) to target were used. Correlations were performed using Spearman's correlation between segmentation evaluation metrics and dosimetric changes.

RESULTS : Only strong (|R2 | > 0.6, p < 0.01) or moderate (|R2 | > 0.4, p < 0.01) Pearson correlation was established between the traditional geometric metric and three dosimetric evaluation indices to target (conformity index, homogeneity index, and mean dose). For organs at risk (OARs), inferior or no significant relationship was found between geometric parameters and dosimetric differences. Furthermore, we found that OARs dose distribution was affected by boundary error of target segmentation instead of distance and R V ${R}_V$ to target.

CONCLUSIONS : Current geometric metrics could reflect a certain degree of dose effect of target variation. To find target contour variations that do lead to OARs dosimetry changes, clinically oriented metrics that more accurately reflect how segmentation quality affects dosimetry should be constructed.

Zhong Yang, Guo Ying, Fang Yingtao, Wu Zhiqiang, Wang Jiazhou, Hu Weigang

2023-Mar-15

auto-segmentation, dosimetric impact, geometric metrics, radiotherapy target