In Plant methods

BACKGROUND : The rapidly advancing corn breeding field calls for high-throughput methods to phenotype corn kernel traits to estimate yield and to study their genetic inheritance. Most of the existing methods are reliant on sophisticated setup, expertise in statistical models and programming skills for image capturing and analysis.

RESULTS : We demonstrated a portable, easily accessible, affordable, panoramic imaging capturing system called Corn360, followed by image analysis using freely available software, to characterize total kernel count and different patterned kernel counts of a corn ear. The software we used did not require programming skills and utilized Artificial Intelligence to train a model and to segment the images of mixed patterned corn ears. For homogeneously patterned corn ears, our results showed accuracies of 93.7% of total kernel count compared to manual counting. Our method allowed to save an average of 3 min 40 s per image. For mixed patterned corn ears, our results showed accuracies of 84.8% or 61.8% of segmented kernel counts. Our method has the potential to greatly decrease counting time per image as the number of images increases. We also demonstrated a case of using Corn360 to count different categories of kernels on a mixed patterned corn ear resulting from a cross of sweet corn and sticky corn and showed that starch:sweet:sticky segregated in a 9:4:3 ratio in its F2 population.

CONCLUSIONS : The panoramic Corn360 approach enables for a portable low-cost high-throughput kernel quantification. This includes total kernel quantification and quantification of different patterned kernels. This can allow for quick estimate of yield component and for categorization of different patterned kernels to study the inheritance of genes controlling color and texture. We demonstrated that using the samples resulting from a sweet × sticky cross, the starchiness, sweetness and stickiness in this case were controlled by two genes with epistatic effects. Our achieved results indicate Corn360 can be used to effectively quantify corn kernels in a portable and cost-efficient way that is easily accessible with or without programming skills.

Gillette Samantha, Yin Lu, Kianian Penny M A, Pawlowski Wojciech P, Chen Changbin

2023-Mar-09

Corn, High-throughput phenotyping, Image analysis, Kernel color, Kernel texture, Low-cost

In BMC psychiatry

BACKGROUND : Major Depressive Disorder (MDD) is a heterogenous brain disorder, with potentially multiple psychosocial and biological disease mechanisms. This is also a plausible explanation for why patients do not respond equally well to treatment with first- or second-line antidepressants, i.e., one-third to one-half of patients do not remit in response to first- or second-line treatment. To map MDD heterogeneity and markers of treatment response to enable a precision medicine approach, we will acquire several possible predictive markers across several domains, e.g., psychosocial, biochemical, and neuroimaging.

METHODS : All patients are examined before receiving a standardised treatment package for adults aged 18-65 with first-episode depression in six public outpatient clinics in the Capital Region of Denmark. From this population, we will recruit a cohort of 800 patients for whom we will acquire clinical, cognitive, psychometric, and biological data. A subgroup (subcohort I, n = 600) will additionally provide neuroimaging data, i.e., Magnetic Resonance Imaging, and Electroencephalogram, and a subgroup of patients from subcohort I unmedicated at inclusion (subcohort II, n = 60) will also undergo a brain Positron Emission Tomography with the [¹¹C]-UCB-J tracer binding to the presynaptic glycoprotein-SV2A. Subcohort allocation is based on eligibility and willingness to participate. The treatment package typically lasts six months. Depression severity is assessed with the Quick Inventory of Depressive Symptomatology (QIDS) at baseline, and 6, 12 and 18 months after treatment initiation. The primary outcome is remission (QIDS ≤ 5) and clinical improvement (≥ 50% reduction in QIDS) after 6 months. Secondary endpoints include remission at 12 and 18 months and %-change in QIDS, 10-item Symptom Checklist, 5-item WHO Well-Being Index, and modified Disability Scale from baseline through follow-up. We also assess psychotherapy and medication side-effects. We will use machine learning to determine a combination of characteristics that best predict treatment outcomes and statistical models to investigate the association between individual measures and clinical outcomes. We will assess associations between patient characteristics, treatment choices, and clinical outcomes using path analysis, enabling us to estimate the effect of treatment choices and timing on the clinical outcome.

DISCUSSION : The BrainDrugs-Depression study is a real-world deep-phenotyping clinical cohort study of first-episode MDD patients.

TRIAL REGISTRATION : Registered at clinicaltrials.gov November 15th, 2022 (NCT05616559).

Jensen Kristian Høj Reveles, Dam Vibeke H, Ganz Melanie, Fisher Patrick MacDonald, Ip Cheng-Teng, Sankar Anjali, Marstrand-Joergensen Maja Rou, Ozenne Brice, Osler Merete, Penninx Brenda W J H, Pinborg Lars H, Frokjaer Vibe Gedsø, Knudsen Gitte Moos, Jørgensen Martin Balslev

2023-Mar-09

Biomarker, Cognition, EEG, MRI, Major depressive disorder, PET, Precision medicine, Psychotherapy, SSRI, Synaptic density

In BMC oral health ; h5-index 40.0

PURPOSE : The aim of this study was to reveal the biological function of endoplasmic reticulum stress (ERS)-related genes (ERSGs) in periodontitis, and provide potential ERS diagnostic markers for clinical therapy of periodontitis.

METHODS : The differentially expressed ERSGs (DE-ERSGs) were reveled based on periodontitis-related microarray dataset in Gene Expression Omnibus (GEO) database and 295 ERS in previous study, followed by a protein-protein interaction network construction. Then, the subtypes of periodontitis were explored, followed by validation with immune cell infiltration and gene set enrichment. Two machine learning algorithms were used to reveal potential ERS diagnostic markers of periodontitis. The diagnostic effect, target drug and immune correlation of these markers were further evaluated. Finally, a microRNA(miRNA)-gene interaction network was constructed.

RESULTS : A total of 34 DE-ERSGs were revealed between periodontitis samples and control, followed by two subtypes investigated. There was a significant difference of ERS score, immune infiltration and Hallmark enrichment between two subtypes. Then, totally 7 ERS diagnostic markers including FCGR2B, XBP1, EDEM2, ATP2A3, ERLEC1, HYOU1 and YOD1 were explored, and the v the time-dependent ROC analysis showed a reliable result. In addition, a drug-gene network was constructed with 4 up-regulated ERS diagnostic markers and 24 drugs. Finally, based on 32 interactions, 5 diagnostic markers and 20 miRNAs, a miRNA-target network was constructed.

CONCLUSIONS : Up-regulated miR-671-5p might take part in the progression of periodontitis via stimulating the expression of ATP2A3. ERSGs including XBP1 and FCGR2B might be novel diagnostic marker for periodontitis.

Sun Qianqian, Zhu Enqiang

2023-Mar-09

Diagnostic markers, Drug-gene network, Endoplasmic reticulum stress, Molecular subtype, Periodontitis, miRNA-gene network

In Surgical endoscopy ; h5-index 65.0

INTRODUCTION : Indocyanine green (ICG) quantification and assessment by machine learning (ML) could discriminate tissue types through perfusion characterisation, including delineation of malignancy. Here, we detail the important challenges overcome before effective clinical validation of such capability in a prospective patient series of quantitative fluorescence angiograms regarding primary and secondary colorectal neoplasia.

METHODS : ICG perfusion videos from 50 patients (37 with benign (13) and malignant (24) rectal tumours and 13 with colorectal liver metastases) of between 2- and 15-min duration following intravenously administered ICG were formally studied (clinicaltrials.gov: NCT04220242). Video quality with respect to interpretative ML reliability was studied observing practical, technical and technological aspects of fluorescence signal acquisition. Investigated parameters included ICG dosing and administration, distance-intensity fluorescent signal variation, tissue and camera movement (including real-time camera tracking) as well as sampling issues with user-selected digital tissue biopsy. Attenuating strategies for the identified problems were developed, applied and evaluated. ML methods to classify extracted data, including datasets with interrupted time-series lengths with inference simulated data were also evaluated.

RESULTS : Definable, remediable challenges arose across both rectal and liver cohorts. Varying ICG dose by tissue type was identified as an important feature of real-time fluorescence quantification. Multi-region sampling within a lesion mitigated representation issues whilst distance-intensity relationships, as well as movement-instability issues, were demonstrated and ameliorated with post-processing techniques including normalisation and smoothing of extracted time-fluorescence curves. ML methods (automated feature extraction and classification) enabled ML algorithms glean excellent pathological categorisation results (AUC-ROC > 0.9, 37 rectal lesions) with imputation proving a robust method of compensation for interrupted time-series data with duration discrepancies.

CONCLUSION : Purposeful clinical and data-processing protocols enable powerful pathological characterisation with existing clinical systems. Video analysis as shown can inform iterative and definitive clinical validation studies on how to close the translation gap between research applications and real-world, real-time clinical utility.

Hardy Niall P, MacAonghusa Pol, Dalli Jeffrey, Gallagher Gareth, Epperlein Jonathan P, Shields Conor, Mulsow Jurgen, Rogers Ailín C, Brannigan Ann E, Conneely John B, Neary Peter M, Cahill Ronan A

2023-Mar-09

Artificial intelligence, Colorectal cancer, Fluorescence-guided surgery, Indocyanine green quantification, Machine learning

In European radiology ; h5-index 62.0

OBJECTIVES : To evaluate and compare the measurement accuracy of two different computer-aided diagnosis (CAD) systems regarding artificial pulmonary nodules and assess the clinical impact of volumetric inaccuracies in a phantom study.

METHODS : In this phantom study, 59 different phantom arrangements with 326 artificial nodules (178 solid, 148 ground-glass) were scanned at 80 kV, 100 kV, and 120 kV. Four different nodule diameters were used: 5 mm, 8 mm, 10 mm, and 12 mm. Scans were analyzed by a deep-learning (DL)-based CAD and a standard CAD system. Relative volumetric errors (RVE) of each system vs. ground truth and the relative volume difference (RVD) DL-based vs. standard CAD were calculated. The Bland-Altman method was used to define the limits of agreement (LOA). The hypothetical impact on LungRADS classification was assessed for both systems.

RESULTS : There was no difference between the three voltage groups regarding nodule volumetry. Regarding the solid nodules, the RVE of the 5-mm-, 8-mm-, 10-mm-, and 12-mm-size groups for the DL CAD/standard CAD were 12.2/2.8%, 1.3/ - 2.8%, - 3.6/1.5%, and - 12.2/ - 0.3%, respectively. The corresponding values for the ground-glass nodules (GGN) were 25.6%/81.0%, 9.0%/28.0%, 7.6/20.6%, and 6.8/21.2%. The mean RVD for solid nodules/GGN was 1.3/ - 15.2%. Regarding the LungRADS classification, 88.5% and 79.8% of all solid nodules were correctly assigned by the DL CAD and the standard CAD, respectively. 14.9% of the nodules were assigned differently between the systems.

CONCLUSIONS : Patient management may be affected by the volumetric inaccuracy of the CAD systems and hence demands supervision and/or manual correction by a radiologist.

KEY POINTS : • The DL-based CAD system was more accurate in the volumetry of GGN and less accurate regarding solid nodules than the standard CAD system. • Nodule size and attenuation have an effect on the measurement accuracy of both systems; tube voltage has no effect on measurement accuracy. • Measurement inaccuracies of CAD systems can have an impact on patient management, which demands supervision by radiologists.

Peters Alan A, Christe Andreas, von Stackelberg Oyunbileg, Pohl Moritz, Kauczor Hans-Ulrich, Heußel Claus Peter, Wielpütz Mark O, Ebner Lukas

2023-Mar-10

Artificial intelligence, Computer-assisted diagnosis, Deep learning, Imaging phantoms, Lung neoplasms

In Oral radiology

OBJECTIVES : This study aimed to automatically determine the cervical vertebral maturation (CVM) processes on lateral cephalometric radiograph images using a proposed deep learning-based convolutional neural network (CNN) model and to test the success rate of this CNN model in detecting CVM stages using precision, recall, and F1-score.

METHODS : A total of 588 digital lateral cephalometric radiographs of patients with a chronological age between 8 and 22 years were included in this study. CVM evaluation was carried out by two dentomaxillofacial radiologists. CVM stages in the images were divided into 6 subgroups according to the growth process. A convolutional neural network (CNN) model was developed in this study. Experimental studies for the developed model were carried out in the Jupyter Notebook environment using the Python programming language, the Keras, and TensorFlow libraries.

RESULTS : As a result of the training that lasted 40 epochs, 58% training and 57% test accuracy were obtained. The model obtained results that were very close to the training on the test data. On the other hand, it was determined that the model showed the highest success in terms of precision and F1-score in the CVM Stage 1 and the highest success in the recall value in the CVM Stage 2.

CONCLUSION : The experimental results have shown that the developed model achieved moderate success and it reached a classification accuracy of 58.66% in CVM stage classification.

Akay Gülsün, Akcayol M Ali, Özdem Kevser, Güngör Kahraman

2023-Mar-09

Artificial intelligence, Cervical vertebrae maturation, Convolutional neural networks, Deep learning