Advancements in Imaging Sensors and AI for Plant Stress Detection: A Systematic Literature Review.

Integrating imaging sensors and artificial intelligence (AI) have contributed to detecting plant stress symptoms, yet data analysis remains a key challenge. Data challenges include standardized data collection, analysis protocols, selection of imaging sensors and AI algorithms, and finally, data sharing. Here, we present a systematic literature review (SLR) scrutinizing plant imaging and AI for identifying stress responses. We performed a scoping review using specific keywords, namely abiotic and biotic stress, machine learning, plant imaging and deep learning. Next, we used programmable bots to retrieve relevant papers published since 2006. In total, 2,704 papers from 4 databases (Springer, ScienceDirect, PubMed, and Web of Science) were found, accomplished by using a second layer of keywords (e.g., hyperspectral imaging and supervised learning). To bypass the limitations of search engines, we selected OneSearch to unify keywords. We carefully reviewed 262 studies, summarizing key trends in AI algorithms and imaging sensors. We demonstrated that the increased availability of open-source imaging repositories such as PlantVillage or Kaggle has strongly contributed to a widespread shift to deep learning, requiring large datasets to train in stress symptom interpretation. Our review presents current trends in AI-applied algorithms to develop effective methods for plant stress detection using image-based phenotyping. For example, regression algorithms have seen substantial use since 2021. Ultimately, we offer an overview of the course ahead for AI and imaging technologies to predict stress responses. Altogether, this SLR highlights the potential of AI imaging in both biotic and abiotic stress detection to overcome challenges in plant data analysis.

Twelve tips for designing and implementing peer-led assessment writing schemes in health professions education.

Peer-led assessment (PLA) has gained increasing prominence within health professions education as an effective means of engaging learners in the process of assessment writing and practice. Involving students in various stages of the assessment lifecycle, including item writing, quality assurance, and feedback, not only facilitates the creation of high-quality item banks with minimal faculty input but also promotes the development of students' assessment literacy and fosters their growth as teachers. The advantages of involving students in the generation of assessments are evident from a pedagogical standpoint, benefiting both students and faculty. However, faculty members may face uncertainty when it comes to implementing such approaches effectively. To address this concern, this paper presents twelve tips that offer guidance on important considerations for the successful implementation of peer-led assessment schemes in the context of health professions education.

Diversity of thought: public perceptions of genetic testing across ethnic groups in the UK.

Genetic testing is becoming rapidly more accessible to the general populous either through or outside healthcare systems. Few large-scale studies have been carried out to gauge public opinion in this growing area. Here, we undertook the largest cross-sectional study on genetic testing in the UK. The primary purpose of this study is to identify the differences in attitudes toward genetic testing across ethnic groups. A cohort of 6500 individuals from a diverse population completed a 72-item survey in a cross-sectional study. Responses between ethnic minority and white individuals in the UK were compared using a wilcoxon rank-sum and chi-square tests. The white cohort was approximately twice as likely to have taken a genetic test and 13% more had heard about genetic testing before the survey. The ethnic minority cohort appeared more apprehensive about the impact of genetic testing on employability. This study highlights that in the UK, significant differences in opinions regarding genetic testing exist between white individuals and ethnic minority individuals. There is an urgent need to develop more inclusive strategies to equally inform individuals from all backgrounds to avoid disparities in the utilisation of genetic testing.

Analysis of ß-nerve growth factor and its precursor during human pregnancy by immunoaffinity-liquid chromatography tandem mass spectrometry.

ß-Nerve growth factor (NGF) is a neurotrophin that plays a critical role in fetal development during gestation. ProNGF is the precursor form of NGF with a distinct biological profile. In order to investigate the role of NGF and proNGF in pregnant human females, a sensitive and selective immunoaffinity liquid chromatography-tandem mass spectrometry assay was developed and qualified to simultaneously measure the levels of total NGF (tNGF; sum of mature and proNGF) and proNGF using full and relative quantification strategies, respectively. The assay was used to determine serum tNGF and proNGF levels in the three gestational trimesters of pregnancy and in non-pregnant female controls. Mean tNGF ± SD were 44.6 ± 12.3, 42.6 ± 9.3, 65.4 ± 17.6 and 77.0 ± 17.8 pg/mL for non-pregnant, first, second, and third trimesters, respectively, demonstrating no significant increase in circulating tNGF between the control and the first trimester, and a moderate yet significant 1.7-fold increase through gestation. proNGF levels during the first trimester were unchanged compared to control. In contrast to tNGF, however, proNGF levels during gestation remained stable without significant changes. The development of this sensitive, novel immunoaffinity duplexed assay for both tNGF and proNGF is expected to enable further elucidation of the roles these neurotrophins play in human pregnancy as well as other models.

Phenotyping for waterlogging tolerance in crops: current trends and future prospects.

Yield losses to waterlogging are expected to become an increasingly costly and frequent issue in some regions of the world. Despite the extensive work that has been carried out examining the molecular and physiological responses to waterlogging, phenotyping for waterlogging tolerance has proven difficult. This difficulty is largely due to the high variability of waterlogging conditions such as duration, temperature, soil type, and growth stage of the crop. In this review, we highlight use of phenotyping to assess and improve waterlogging tolerance in temperate crop species. We start by outlining the experimental methods that have been utilized to impose waterlogging stress, ranging from highly controlled conditions of hydroponic systems to large-scale screenings in the field. We also describe the phenotyping traits used to assess tolerance ranging from survival rates and visual scoring to precise photosynthetic measurements. Finally, we present an overview of the challenges faced in attempting to improve waterlogging tolerance, the trade-offs associated with phenotyping in controlled conditions, limitations of classic phenotyping methods, and future trends using plant-imaging methods. If effectively utilized to increase crop resilience to changing climates, crop phenotyping has a major role to play in global food security.

Capturing crop adaptation to abiotic stress using image-based technologies.

Farmers and breeders aim to improve crop responses to abiotic stresses and secure yield under adverse environmental conditions. To achieve this goal and select the most resilient genotypes, plant breeders and researchers rely on phenotyping to quantify crop responses to abiotic stress. Recent advances in imaging technologies allow researchers to collect physiological data non-destructively and throughout time, making it possible to dissect complex plant responses into quantifiable traits. The use of image-based technologies enables the quantification of crop responses to stress in both controlled environmental conditions and field trials. This paper summarizes phenotyping imaging technologies (RGB, multispectral and hyperspectral sensors, among others) that have been used to assess different abiotic stresses including salinity, drought and nitrogen deficiency, while discussing their advantages and drawbacks. We present a detailed review of traits involved in abiotic tolerance, which have been quantified by a range of imaging sensors under high-throughput phenotyping facilities or using unmanned aerial vehicles in the field. We also provide an up-to-date compilation of spectral tolerance indices and discuss the progress and challenges in machine learning, including supervised and unsupervised models as well as deep learning.

Takotsubo syndrome and complete heart block, which came first? A case report.

BACKGROUND: In 2018, the European Society of Cardiology published two consensus documents on takotsubo syndrome (TTS), which include the current consensus on nomenclature, diagnosis, management, and complications. However, little is mentioned on the association with complete heart block (CHB), except that 'AV block [occurs in] 2.9% of cases'. Complete heart block is a recognized rare association of TTS, but causation is often unclear. Does CHB trigger TTS or vice-versa? Here, we present a case of TTS associated with CHB. CASE SUMMARY: An 89-year-old woman presented with a transient loss of consciousness, acute chest pain, and dyspnoea. A few days prior to this her daughter died suddenly of a myocardial infarction. On presentation, troponin levels were elevated, the electrocardiogram showed CHB with a broad QRS and an echo showed apical akinesis and ballooning. Angiographic investigation excluded significant coronary artery disease. A dual-chamber pacemaker was implanted after a brief period of temporary pacing. Ventricular function normalized during follow-up and her underlying rhythm remained CHB. DISCUSSION: Takotsubo syndrome may be triggered by both emotional and physical stressors. Complete heart block is recognized association, but causation is often unclear. In our case, a clear emotional trigger was identified suggesting the TTS may have precipitated CHB not vice versa.

Dystrophin and mini-dystrophin quantification by mass spectrometry in skeletal muscle for gene therapy development in Duchenne muscular dystrophy.

Duchenne muscular dystrophy (DMD) is a lethal, degenerative muscle disorder caused by mutations in the DMD gene, leading to severe reduction or absence of the protein dystrophin. Gene therapy strategies that aim to increase expression of a functional dystrophin protein (mini-dystrophin) are under investigation. The ability to accurately quantify dystrophin/mini-dystrophin is essential in assessing the level of gene transduction. We demonstrated the validation and application of a novel peptide immunoaffinity liquid chromatography-tandem mass spectrometry (IA-LC-MS/MS) assay. Data showed that dystrophin expression in Becker muscular dystrophy and DMD tissues, normalized against the mean of non-dystrophic control tissues (n = 20), was 4-84.5% (mean 32%, n = 20) and 0.4-24.1% (mean 5%, n = 20), respectively. In a DMD rat model, biceps femoris tissue from dystrophin-deficient rats treated with AAV9.hCK.Hopti-Dys3978.spA, an adeno-associated virus vector containing a mini-dystrophin transgene, showed a dose-dependent increase in mini-dystrophin expression at 6 months post-dose, exceeding wildtype dystrophin levels at high doses. Validation data showed that inter- and intra-assay precision were ≤20% (≤25% at the lower limit of quantification [LLOQ]) and inter- and intra-run relative error was within ±20% (±25% at LLOQ). IA-LC-MS/MS accurately quantifies dystrophin/mini-dystrophin in human and preclinical species with sufficient sensitivity for immediate application in preclinical/clinical trials.

A hybrid modeling approach for assessing mechanistic models of small molecule partitioning in vivo using a machine learning-integrated modeling platform.

Prediction of the first-in-human dosing regimens is a critical step in drug development and requires accurate quantitation of drug distribution. Traditional in vivo studies used to characterize clinical candidate's volume of distribution are error-prone, time- and cost-intensive and lack reproducibility in clinical settings. The paper demonstrates how a computational platform integrating machine learning optimization with mechanistic modeling can be used to simulate compound plasma concentration profile and predict tissue-plasma partition coefficients with high accuracy by varying the lipophilicity descriptor logP. The approach applied to chemically diverse small molecules resulted in comparable geometric mean fold-errors of 1.50 and 1.63 in pharmacokinetic outputs for direct tissue:plasma partition and hybrid logP optimization, with the latter enabling prediction of tissue permeation that can be used to guide toxicity and efficacy dosing in human subjects. The optimization simulations required to achieve these results were parallelized on the AWS cloud and generated outputs in under 5 h. Accuracy, speed, and scalability of the framework indicate that it can be used to assess the relevance of other mechanistic relationships implicated in pharmacokinetic-pharmacodynamic phenomena with a lower risk of overfitting datasets and generate large database of physiologically-relevant drug disposition for further integration with machine learning models.

Scalable in silico Simulation of Transdermal Drug Permeability: Application of BIOiSIM Platform.

INTRODUCTION: Transdermal drug delivery is gaining popularity as an alternative to traditional routes of administration. It can increase patient compliance because of its painless and noninvasive nature, aid compounds in bypassing presystemic metabolic effects, and reduce the likelihood of adverse effects through decreased systemic exposure. In silico physiological modeling is critical to predicting dermal exposure for a therapeutic and assessing the impact of different formulations on transdermal disposition. METHODS: The present study aimed at developing a physiologically based transdermal platform, "BIOiSIM", that could be globally applied to a wide variety of compounds to predict their transdermal disposition. The platform integrates a 16-compartment model of compound pharmacokinetics and was used to simulate and predict drug exposure of three chemically and biologically distinct drug-like compounds. Machine learning optimization was composed of two components: exhaustive search algorithm (coarse-tuning) and descent (fine-tuning) integrated with the platform used to quantitatively determine parameters influencing pharmacokinetics (eg permeability, kperm) of test compounds. RESULTS: The model successfully predicted drug exposure (AUC, Cmax and Tmax) following transdermal application of morphine, buprenorphine and nicotine in human subjects, mostly with less than two-fold absolute average fold error (AAFE). The model was further able to successfully characterize the relationship between observed systemic exposure and intended pharmacological effect. The predicted systemic concentration of morphine and plasma levels of endogenous pain biomarkers were used to estimate the effectiveness of a given therapeutic regimen. CONCLUSION: BIOiSIM marks a novel approach to in silico prediction that will enable leveraging of machine learning technology in the pharmaceutical space. The approach to model development outlined results in scalable, accurate models and enables the generation of large parameter/coefficient datasets from in vivo clinical data that can be used in future work to train quantitative structure activity relationship (QSAR) models for predicting likelihood of compound utility as a transdermally administered therapeutic.

Pediatric Drowning: A Standard Operating Procedure to Aid the Prehospital Management of Pediatric Cardiac Arrest Resulting From Submersion.

OBJECTIVES: Drowning is one of the leading causes of death in children. Resuscitating a child following submersion is a high-pressure situation, and standard operating procedures can reduce error. Currently, the Resuscitation Council UK guidance does not include a standard operating procedure on pediatric drowning. The objective of this project was to design a standard operating procedure to improve outcomes of drowned children. METHODS: A literature review on the management of pediatric drowning was conducted. Relevant publications were used to develop a standard operating procedure for management of pediatric drowning. RESULTS: A concise standard operating procedure was developed for resuscitation following pediatric submersion. Specific recommendations include the following: the Heimlich maneuver should not be used in this context; however, prolonged resuscitation and therapeutic hypothermia are recommended. CONCLUSIONS: This standard operating procedure is a potentially useful adjunct to the Resuscitation Council UK guidance and should be considered for incorporation into its next iteration.

A speckle-tracking strain-based artificial neural network model to differentiate cardiomyopathy type.

Objectives. In heart failure, invasive angiography is often employed to differentiate ischaemic from non-ischaemic cardiomyopathy. We aim to examine the predictive value of echocardiographic strain features alone and in combination with other features to differentiate ischaemic from non-ischaemic cardiomyopathy, using artificial neural network (ANN) and logistic regression modelling. Design. We retrospectively identified 204 consecutive patients with an ejection fraction <50% and a diagnostic angiogram. Patients were categorized as either ischaemic (n = 146) or non-ischaemic cardiomyopathy (n = 58). For each patient, left ventricular strain parameters were obtained. Additionally, regional wall motion abnormality, 13 electrocardiographic (ECG) features and six demographic features were retrieved for analysis. The entire cohort was randomly divided into a derivation and a validation cohort. Using the parameters retrieved, logistic regression and ANN models were developed in the derivation cohort to differentiate ischaemic from non-ischaemic cardiomyopathy, the models were then tested in the validation cohort. Results. A final strain-based ANN model, full feature ANN model and full feature logistic regression model were developed and validated, F1 scores were 0.82, 0.79 and 0.63, respectively. Conclusions. Both ANN models were more accurate at predicting cardiomyopathy type than the logistic regression model. The strain-based ANN model should be validated in other cohorts. This model or similar models could be used to aid the diagnosis of underlying heart failure aetiology in the form of the online calculator (https://cimti.usj.edu.lb/strain/index.html) or built into echocardiogram software.

Hereditary haemorrhagic telangiectasia with multiple complications presenting in an older patient.

Hereditary haemorrhagic telangiectasia (HHT) is a rare autosomal dominant multi-organ vascular disorder associated with bleeding and a reduced life expectancy. We present a 91-year-old woman with complications of previously undiagnosed HHT. This case demonstrates three potential complications: pulmonary arteriovenous malformation (AVM) resulting in a right to left shunt, cerebral infarctions and pericardial effusion. Despite these potentially life-threatening complications and the reduced life-expectancy associated with HHT the patient has survived to an advanced age. Due to the patient's late diagnosis and frailty, the treatment options of AVM embolization and pericardiocentesis were deemed inappropriate. The patient was treated with tranexamic acid to reduce bleeding severity and discharged home with a care package and home oxygen.

Impact of a hospital-based educational intervention on dietary salt-related knowledge and behaviour in a cardiac care unit population in Lebanon.

BACKGROUND: Cardiovascular disease is the most common cause of morbidity and mortality worldwide. Numerous strategies have been effective in reducing cardiovascular disease risk, from pharmacological approaches to lifestyle modification interventions. One of these strategies includes the reduction in dietary sodium which in turn reduces cardiovascular risk by reducing high blood pressure, perhaps the most important cardiovascular risk factor. METHODS: We evaluated an educational dietary salt reduction intervention in a cardiac care unit population in Lebanon, assessing salt related knowledge and behaviours before and after administering an evidence-based educational leaflet to patients. RESULTS: Salt-related knowledge improved significantly immediately post-intervention and subsequently fell on 4-week follow-up, but remained above baseline. Three of the four salt-related behaviours measured improved on 4-week follow-up: trying to buy low-salt foods increased from 54% to 74% (P=0.007), adding salt at the table reduced from 44% to 34% (P=0.03) and trying to buy food with no added salt increased from 24% to 52% (P=0.02) of the cohort. Adding salt during cooking did not differ significantly. A trend towards improved behavioural risk category in the cohort overall was observed on follow-up (P=0.07), 32% of participants were categorised as high behavioural risk pre-intervention, reducing to 17% on follow-up. Multi-ordered regression modelling identified being in the high-risk behavioural category at baseline as a predictor of being in the high-risk or moderate-risk category on follow-up. CONCLUSIONS: This hospital-based educational intervention had a modestly positive impact on salt-related knowledge and behaviour, with participants in the highest behavioural risk category at baseline being most resistant to behavioural improvement.

Formative student-authored question bank: perceptions, question quality and association with summative performance.

PURPOSE OF THE STUDY: There are few studies on the value of authoring questions as a study method, the quality of the questions produced by students and student perceptions of student-authored question banks. Here we evaluate PeerWise, a widely used and free online resource that allows students to author, answer and discuss multiple-choice questions. STUDY DESIGN: We introduced two undergraduate medical student cohorts to PeerWise (n=603). We looked at their patterns of PeerWise usage; identified associations between student engagement and summative exam performance; and used focus groups to assess student perceptions of the value of PeerWise for learning. We undertook item analysis to assess question difficulty and quality. RESULTS: Over two academic years, the two cohorts wrote 4671 questions, answered questions 606 658 times and posted 7735 comments. Question writing frequency correlated most strongly with summative performance (Spearman's rank: 0.24, p=<0.001). Student focus groups found that: (1) students valued curriculum specificity; and (2) students were concerned about student-authored question quality. Only two questions of the 300 'most-answered' questions analysed had an unacceptable discriminatory value (point-biserial correlation <0.2). CONCLUSIONS: Item analysis suggested acceptable question quality despite student concerns. Quantitative and qualitative methods indicated that PeerWise is a valuable study tool.

Identification of methylated metabolites of oat avenanthramides in human plasma using UHPLC QToF-MS.

Oat avenanthramides (AVAs) are a group of phenolic alkaloids, consisting of an anthranilic acid and a hydroxycinnamic acid linked by a pseudo-peptide bond. Bioavailability of AVA is poor in humans, suggesting transformations for rapid excretion. Thus, we aim to identify metabolites of AVA isomers in plasma of humans after consuming AVA-enriched oats. After lipid removal, AVA and their metabolites in plasma were extracted with ethyl acetate and analysed using an Agilent UHPLC-QToF-MS. Pharmacokinetics of AVA-O showed a bimodal distribution with Cmax1 and 2 for AVA-O at 5.9 ± 5.2 and 7.9 ± 7.0 ng/mL and Tmax1 and 2 at 1.7 ± 0.7 and 3.1 ± 1.2 h, respectively. Only the methyl-AVA-O showed a single Cmax at 14 ± 9.9 ng/mL AVA-O equivalents and a Tmax of 2.4 ± 2.7 h. This analysis is the first to identify methylated metabolites of AVAs and AVA aglycones in human blood after acute AVA consumption.