Assessment of the Capability to Accurately Capture the Vertical Relationship by the Various Bite Registration Materials.

Background: This research study aimed to evaluate and compare the capability of four various bite registration materials to reproduce precise interocclusal relationships in the vertical dimension. Materials and Methods: Ideal maxillary and mandibular casts were mounted on the semi-adjustable articulator in maximum intercuspation after mock tooth preparation on 46, 47, and 48. Models were scanned by the Medit T500 Dental Lab Scanner, and initial reading was noted at the predetermined points. Ten interocclusal bite registrations were made using four materials (CADbite, Jet Bite, Ramitec, and Aluwax). The mandibular model was demounted and again remounted using the interocclusal records, and the final reading was noted after scanning. Results: Ramitec showed superior results when compared to polyvinyl bite registration material and Aluwax, but the differences between Ramitec, CADbite, and Jet Bite were nonsignificant. Conclusions: Although all four materials are suitable for clinical use, elastomeric materials showed superior results. In that, polyether was found to be the best.

Operational priorities for engaging with India's private healthcare sector for the control of tuberculosis: a modelling study.

OBJECTIVES: To estimate the potential impact of expanding services offered by the Joint Effort for Elimination of Tuberculosis (JEET), the largest private sector engagement initiative for tuberculosis (TB) in India. DESIGN: We developed a mathematical model of TB transmission dynamics, coupled with a cost model. SETTING: Ahmedabad and New Delhi, two cities with contrasting levels of JEET coverage. PARTICIPANTS: Estimated patients with TB in Ahmedabad and New Delhi. INTERVENTIONS: We investigated the epidemiological impact of expanding three different public-private support agency (PPSA) services: provider recruitment, uptake of cartridge-based nucleic acid amplification tests and uptake of adherence support mechanisms (specifically government supplied fixed-dose combination drugs), all compared with a continuation of current TB services. RESULTS: Our results suggest that in Delhi, increasing the use of adherence support mechanisms among private providers should be prioritised, having the lowest incremental cost-per-case-averted between 2020 and 2035 of US$170 000 (US$110 000-US$310 000). Likewise in Ahmedabad, increasing provider recruitment should be prioritised, having the lowest incremental cost-per-case averted of US$18 000 (US$12 000-US$29 000). CONCLUSION: Results illustrate how intervention priorities may vary in different settings across India, depending on local conditions, and the existing degree of uptake of PPSA services. Modelling can be a useful tool for identifying these priorities for any given setting.

Investigating the Role of Amino Acids in Short Peptides for Hydroxyapatite Binding and Osteogenic Differentiation of Mesenchymal Stem Cells to Aid Bone Regeneration.

Bone defects show a slow rate of osteoconduction and imperfect reconstruction, and the current treatment strategies to treat bone defects suffer from limitations like immunogenicity, lack of cell adhesion, and the absence of osteogenic activity. In this context, bioactive supramolecular peptides and peptide gels offer unique opportunities to develop biomaterials that can play a dominant role in the biomineralization of bone tissues and promote bone formation. In this article, we have demonstrated the potential of six tetrapeptides for specific binding to hydroxyapatite (HAp), a major inorganic component of the bone, and their effect on the growth and osteogenic differentiation of mesenchymal stem cells (MSCs). We adopted a simplistic approach of rationally designing amphiphilic peptides by incorporating amino acids, Ser, pSer, Pro, Hyp, Asp, and Glu, which are present in either collagenous or noncollagenous proteins and render properties like antioxidant, calcification, and mineralization. A total of six tetrapeptides, Trp-Trp-His-Ser (WWHS), Trp-Trp-His-pSer (WWHJ), Trp-Trp-His-Pro (WWHP), Trp-Trp-His-Hyp (WWHO), Trp-Trp-His-Asp (WWHD), and Trp-Trp-His-Glu (WWHE), were synthesized. Four peptides were found to self-assemble into nanofibrillar gels resembling the extracellular matrix (ECM), and the remaining two peptides (WWHJ, WWHP) self-assembled into nanorods. The peptides showed excellent cell adhesion, encapsulation, proliferation, and migration and induced the differentiation of mesenchymal stem cells (MSCs), as evident from the enhanced mineralization, resulting from the upregulation of osteogenic markers, RUNX 2, COL I, OPN, and OCN, alkaline phosphatase (ALP) production, and calcium deposition. The peptides also induced the downregulation of inflammatory markers, TNF-α and iNOS, and the upregulation of the anti-inflammatory marker, IL-10, resulting in M2 macrophage polarization. RANKL and TRAP genes were downregulated in a coculture system of MC3T3-E1 and RAW 264.7 cells, implying that peptides promote osteogenesis and inhibit osteoclastogenesis. The peptide-based biomaterials developed in this work can enhance bone regeneration capacity and show strong potential as scaffolds for bone tissue engineering.

Downward myocardial creep during stress PET imaging is inversely associated with mortality.

PURPOSE: The myocardial creep is a phenomenon in which the heart moves from its original position during stress-dynamic PET myocardial perfusion imaging (MPI) that can confound myocardial blood flow measurements. Therefore, myocardial motion correction is important to obtain reliable myocardial flow quantification. However, the clinical importance of the magnitude of myocardial creep has not been explored. We aimed to explore the prognostic value of myocardial creep quantified by an automated motion correction algorithm beyond traditional PET-MPI imaging variables. METHODS: Consecutive patients undergoing regadenoson rest-stress [82Rb]Cl PET-MPI were included. A newly developed 3D motion correction algorithm quantified myocardial creep, the maximum motion at stress during the first pass (60 s), in each direction. All-cause mortality (ACM) served as the primary endpoint. RESULTS: A total of 4,276 patients (median age 71 years; 60% male) were analyzed, and 1,007 ACM events were documented during a 5-year median follow-up. Processing time for automatic motion correction was < 12 s per patient. Myocardial creep in the superior to inferior (downward) direction was greater than the other directions (median, 4.2 mm vs. 1.3-1.7 mm). Annual mortality rates adjusted for age and sex were reduced with a larger downward creep, with a 4.2-fold ratio between the first (0 mm motion) and 10th decile (11 mm motion) (mortality, 7.9% vs. 1.9%/year). Downward creep was associated with lower ACM after full adjustment for clinical and imaging parameters (adjusted hazard ratio, 0.93; 95%CI, 0.91-0.95; p < 0.001). Adding downward creep to the standard PET-MPI imaging model significantly improved ACM prediction (area under the receiver operating characteristics curve, 0.790 vs. 0.775; p < 0.001), but other directions did not (p > 0.5). CONCLUSIONS: Downward myocardial creep during regadenoson stress carries additional information for the prediction of ACM beyond conventional flow and perfusion PET-MPI. This novel imaging biomarker is quantified automatically and rapidly from stress dynamic PET-MPI.

Automated Motion Correction for Myocardial Blood Flow Measurements and Diagnostic Performance of 82Rb PET Myocardial Perfusion Imaging.

Motion correction (MC) affects myocardial blood flow (MBF) measurements in 82Rb PET myocardial perfusion imaging (MPI); however, frame-by-frame manual MC of dynamic frames is time-consuming. This study aims to develop an automated MC algorithm for time-activity curves used in compartmental modeling and compare the predictive value of MBF with and without automated MC for significant coronary artery disease (CAD). Methods: In total, 565 patients who underwent PET-MPI were considered. Patients without angiographic findings were split into training (n = 112) and validation (n = 112) groups. The automated MC algorithm used simplex iterative optimization of a count-based cost function and was developed using the training group. MBF measurements with automated MC were compared with those with manual MC in the validation group. In a separate cohort, 341 patients who underwent PET-MPI and invasive coronary angiography were enrolled in the angiographic group. The predictive performance in patients with significant CAD (≥70% stenosis) was compared between MBF measurements with and without automated MC. Results: In the validation group (n = 112), MBF measurements with automated and manual MC showed strong correlations (r = 0.98 for stress MBF and r = 0.99 for rest MBF). The automatic MC took less time than the manual MC (<12 s vs. 10 min per case). In the angiographic group (n = 341), MBF measurements with automated MC decreased significantly compared with those without (stress MBF, 2.16 vs. 2.26 mL/g/min; rest MBF, 1.12 vs. 1.14 mL/g/min; MFR, 2.02 vs. 2.10; all P < 0.05). The area under the curve (AUC) for the detection of significant CAD by stress MBF with automated MC was higher than that without (AUC, 95% CI, 0.76 [0.71-0.80] vs. 0.73 [0.68-0.78]; P < 0.05). The addition of stress MBF with automated MC to the model with ischemic total perfusion deficit showed higher diagnostic performance for detection of significant CAD (AUC, 95% CI, 0.82 [0.77-0.86] vs. 0.78 [0.74-0.83]; P = 0.022), but the addition of stress MBF without MC to the model with ischemic total perfusion deficit did not reach significance (AUC, 95% CI, 0.81 [0.76-0.85] vs. 0.78 [0.74-0.83]; P = 0.067). Conclusion: Automated MC on 82Rb PET-MPI can be performed rapidly with excellent agreement with experienced operators. Stress MBF with automated MC showed significantly higher diagnostic performance than without MC.

Biogas production potential of aquatic weeds as the next-generation feedstock for bioenergy production: a review.

Aquatic weeds have exceptionally high reproduction rates, are rich in cellulose and hemicellulose, and contain a negligible amount of lignin, making them an ideal crop for the next generation of biofuels. Previously reported studies proposed that water hyacinth, water lettuce, common duckweeds, and water spinach can be managed or utilized using different advanced techniques; from them, anaerobic digestion is one of the feasible and cost-effective techniques to manage these biowastes. The present study was carried out to investigate the potential of utilizing four common aquatic weed species (water hyacinth, water lettuce, common duckweeds, and water spinach) as substrates for anaerobic digestion in order to produce biogas for use in biofuels. The high reproduction rates and high cellulose and hemicellulose content, coupled with low lignin content, of these aquatic weeds make them ideal candidates for this purpose. The study evaluated the feasibility of using anaerobic digestion as a management technique for these aquatic weeds, which are often considered invasive and difficult to control. The results from various studies indicate that these aquatic weeds are productive feedstock options for anaerobic digestion, yielding a high biogas output. Among the aquatic weeds studied, water hyacinth, water lettuce, and common duckweeds exhibit higher methane production compared to water spinach. The study provides an overview of the characteristics and management strategies of these aquatic weeds in relation to biogas production, with possible future developments in the field.

Prosthodontic Rehabilitation With Kennedy's Class I and Class II Using an Extended Precision Attachment: A Report of Two Cases.

Satisfactory restorations can be difficult in partially edentulous patients, especially those with unilateral or bilateral posterior ocular defects. With traditional and modern treatment options, recovery can be successful. Partial dentures with attachments are such a treatment. An implant-supported prosthesis is another option for therapy in these circumstances. Precision extracoronary attachments are the preferred treatment option when implant treatment does not give good results. This research offers two examples of partial cast prosthetic rehabilitation for distal extension utilizing precise attachments.

Incremental prognostic value of stress phase entropy over standard PET myocardial perfusion imaging variables.

PURPOSE: Phase analysis can assess left ventricular dyssynchrony. The independent prognostic value of phase variables over positron emission tomography myocardial perfusion imaging (PET-MPI) variables including myocardial flow reserve (MFR) has not been studied. The aim of this study was to explore the prognostic value of phase variables for predicting mortality over standard PET-MPI variables. METHODS: Consecutive patients who underwent pharmacological stress-rest 82Rb PET study were enrolled. All PET-MPI variables including phase variables (phase entropy, phase bandwidth, and phase standard deviation) were automatically obtained by QPET software (Cedars-Sinai, Los Angeles, CA). Cox proportional hazard analyses were used to assess associations with all-cause mortality (ACM). RESULTS: In a total of 3963 patients (median age 71 years; 57% male), 923 patients (23%) died during a median follow-up of 5 years. Annualized mortality rates increased with stress phase entropy, with a 4.6-fold difference between the lowest and highest decile groups of entropy (2.6 vs. 12.0%/year). Abnormal stress phase entropy (optimal cutoff value, 43.8%) stratified ACM risk in patients with normal and impaired MFR (both p < 0.001). Among three phase variables, only stress phase entropy was significantly associated with ACM after the adjustment of standard clinical and PET-MPI variables including MFR and stress-rest change of phase variables, whether modeled as binary variables (adjusted hazard ratio, 1.44 for abnormal entropy [> 43.8%]; 95%CI, 1.18-1.75; p < 0.001) or continuous variables (adjusted hazard ratio, 1.05 per 5% increase; 95%CI, 1.01-1.10; p = 0.030). The addition of stress phase entropy to the standard PET-MPI variables significantly improved the discriminatory power for ACM prediction (p < 0.001), but the other phase variables did not (p > 0.1). CONCLUSION: Stress phase entropy is independently and incrementally associated with ACM beyond standard PET-MPI variables including MFR. Phase entropy can be obtained automatically and included in clinical reporting of PET-MPI studies to improve patient risk prediction.

Artificial Intelligence for Computer-Aided Drug Discovery.

The continuous implementation of Artificial Intelligence (AI) in multiple scientific domains and the rapid advancement in computer software and hardware, along with other parameters, have rapidly fuelled this development. The technology can contribute effectively in solving many challenges and constraints in the traditional development of the drug. Traditionally, large-scale chemical libraries are screened to find one promising medicine. In recent years, more reasonable structure-based drug design approaches have avoided the first screening phases while still requiring chemists to design, synthesize, and test a wide range of compounds to produce possible novel medications. The process of turning a promising chemical into a medicinal candidate can be expensive and time-consuming. Additionally, a new medication candidate may still fail in clinical trials even after demonstrating promise in laboratory research. In fact, less than 10% of medication candidates that undergo Phase I trials really reach the market. As a consequence, the unmatched data processing power of AI systems may expedite and enhance the drug development process in four different ways: by opening up links to novel biological systems, superior or distinctive chemistry, greater success rates, and faster and less expensive innovation trials. Since these technologies may be used to address a variety of discovery scenarios and biological targets, it is essential to comprehend and distinguish between use cases. As a result, we have emphasized how AI may be used in a variety of areas of the pharmaceutical sciences, including in-depth opportunities for drug research and development.

Time and event-specific deep learning for personalized risk assessment after cardiac perfusion imaging.

Standard clinical interpretation of myocardial perfusion imaging (MPI) has proven prognostic value for predicting major adverse cardiovascular events (MACE). However, personalizing predictions to a specific event type and time interval is more challenging. We demonstrate an explainable deep learning model that predicts the time-specific risk separately for all-cause death, acute coronary syndrome (ACS), and revascularization directly from MPI and 15 clinical features. We train and test the model internally using 10-fold hold-out cross-validation (n = 20,418) and externally validate it in three separate sites (n = 13,988) with MACE follow-ups for a median of 3.1 years (interquartile range [IQR]: 1.6, 3.6). We evaluate the model using the cumulative dynamic area under receiver operating curve (cAUC). The best model performance in the external cohort is observed for short-term prediction - in the first six months after the scan, mean cAUC for ACS and all-cause death reaches 0.76 (95% confidence interval [CI]: 0.75, 0.77) and 0.78 (95% CI: 0.78, 0.79), respectively. The model outperforms conventional perfusion abnormality measures at all time points for the prediction of death in both internal and external validations, with improvement increasing gradually over time. Individualized patient explanations are visualized using waterfall plots, which highlight the contribution degree and direction for each feature. This approach allows the derivation of individual event probability as a function of time as well as patient- and event-specific risk explanations that may help draw attention to modifiable risk factors. Such a method could help present post-scan risk assessments to the patient and foster shared decision-making.

Opportunistic optimization of inhaler technique in hospitalized adults with asthma: a two-phase educational study.

OBJECTIVE: To investigate effectiveness of two different educational methods to improve inhaler techniques in patients with prior diagnosis of asthma, hospitalized with a non-asthma-related diagnosis. METHODS: We undertook a real-world, opportunistic quality-improvement project. Inhaler technique in hospitalized patients with prior diagnosis of asthma was assessed in two cohorts over two 12-week cycles using a standardized device-specific proforma of seven-step inhaler technique, classed: "good" if 6/7 steps achieved; "fair" if 5/7 compliant; "poor" for others. Baseline data was collected in both cycles. Cycle one involved face-to-face education by a healthcare professional; cycle two involved additional use of an electronic device to show device-specific videos (asthma.org.uk). In both cycles, patients were reassessed within two days for improvements and the two methods compared for effectiveness. RESULTS: During cycle one 32/40 patients were reassessed within 48 h; eight lost to follow-up. During cycle two 38/40 patients were reassessed within 48 h; two lost to follow-up During cycle one, two and 12 had good/fair baseline technique respectively, and 26 poor. Most commonly missed steps were no expiry check/not rinsing mouth after steroid use. On reassessment 17% patients improved from poor to fair/good. During cycle two, initial technique assessment identified: 23 poor; 12 fair; five good. Post-videos, 35% of patients improved from poor to fair/good. Proportion of patients improving from poor to fair, or poor/fair to good increased in cycle two vs one (52.5% vs 33%). CONCLUSION: Visual instruction is associated with improved technique compared to verbal feedback. This is a user-friendly and cost-effective approach to patient education.

Automated nonlinear registration of coronary PET to CT angiography using pseudo-CT generated from PET with generative adversarial networks.

BACKGROUND: Coronary 18F-sodium-fluoride (18F-NaF) positron emission tomography (PET) showed promise in imaging coronary artery disease activity. Currently image processing remains subjective due to the need for manual registration of PET and computed tomography (CT) angiography data. We aimed to develop a novel fully automated method to register coronary 18F-NaF PET to CT angiography using pseudo-CT generated by generative adversarial networks (GAN). METHODS: A total of 169 patients, 139 in the training and 30 in the testing sets were considered for generation of pseudo-CT from non-attenuation corrected (NAC) PET using GAN. Non-rigid registration was used to register pseudo-CT to CT angiography and the resulting transformation was used to align PET with CT angiography. We compared translations, maximal standard uptake value (SUVmax) and target to background ratio (TBRmax) at the location of plaques, obtained after observer and automated alignment. RESULTS: Automatic end-to-end registration was performed for 30 patients with 88 coronary vessels and took 27.5 seconds per patient. Difference in displacement motion vectors between GAN-based and observer-based registration in the x-, y-, and z-directions was 0.8 ± 3.0, 0.7 ± 3.0, and 1.7 ± 3.9 mm, respectively. TBRmax had a coefficient of repeatability (CR) of 0.31, mean bias of 0.03 and narrow limits of agreement (LOA) (95% LOA: - 0.29 to 0.33). SUVmax had CR of 0.26, mean bias of 0 and narrow LOA (95% LOA: - 0.26 to 0.26). CONCLUSION: Pseudo-CT generated by GAN are perfectly registered to PET can be used to facilitate quick and fully automated registration of PET and CT angiography.

Direct Risk Assessment From Myocardial Perfusion Imaging Using Explainable Deep Learning.

BACKGROUND: Myocardial perfusion imaging (MPI) is frequently used to provide risk stratification, but methods to improve the accuracy of these predictions are needed. OBJECTIVES: The authors developed an explainable deep learning (DL) model (HARD MACE [major adverse cardiac events]-DL) for the prediction of death or nonfatal myocardial infarction (MI) and validated its performance in large internal and external testing groups. METHODS: Patients undergoing single-photon emission computed tomography MPI were included, with 20,401 patients in the training and internal testing group (5 sites) and 9,019 in the external testing group (2 different sites). HARD MACE-DL uses myocardial perfusion, motion, thickening, and phase polar maps combined with age, sex, and cardiac volumes. The primary outcome was all-cause mortality or nonfatal MI. Prognostic accuracy was evaluated using area under the receiver-operating characteristic curve (AUC). RESULTS: During internal testing, patients with normal perfusion and elevated HARD MACE-DL risk were at higher risk than patients with abnormal perfusion and low HARD MACE-DL risk (annualized event rate, 2.9% vs 1.2%; P < 0.001). Patients in the highest quartile of HARD MACE-DL score had an annual rate of death or MI (4.8%) 10-fold higher than patients in the lowest quartile (0.48% per year). In external testing, the AUC for HARD MACE-DL (0.73; 95% CI: 0.71-0.75) was higher than a logistic regression model (AUC: 0.70), stress total perfusion deficit (TPD) (AUC: 0.65), and ischemic TPD (AUC: 0.63; all P < 0.01). Calibration, a measure of how well predicted risk matches actual risk, was excellent in both groups (Brier score, 0.079 for internal and 0.070 for external). CONCLUSIONS: The DL model predicts death or MI directly from MPI, by estimating patient-level risk with good calibration and improved accuracy compared with traditional quantitative approaches. The model incorporates mechanisms to explain to the physician which image regions contribute to the adverse event prediction.

Deep Learning of Coronary Calcium Scores From PET/CT Attenuation Maps Accurately Predicts Adverse Cardiovascular Events.

BACKGROUND: Assessment of coronary artery calcium (CAC) by computed tomographic (CT) imaging provides an accurate measure of atherosclerotic burden. CAC is also visible in computed tomographic attenuation correction (CTAC) scans, always acquired with cardiac positron emission tomographic (PET) imaging. OBJECTIVES: The aim of this study was to develop a deep-learning (DL) model capable of fully automated CAC definition from PET CTAC scans. METHODS: The novel DL model, originally developed for video applications, was adapted to rapidly quantify CAC. The model was trained using 9,543 expert-annotated CT scans and was tested in 4,331 patients from an external cohort undergoing PET/CT imaging with major adverse cardiac events (MACEs) (follow-up 4.3 years), including same-day paired electrocardiographically gated CAC scans available in 2,737 patients. MACE risk stratification in 4 CAC score categories (0, 1-100, 101-400, and >400) was analyzed and CAC scores derived from electrocardiographically gated CT scans (standard scores) by expert observers were compared with automatic DL scores from CTAC scans. RESULTS: Automatic DL scoring required <6 seconds per scan. DL CTAC scores provided stepwise increase in the risk for MACE across the CAC score categories (HR up to 3.2; P < 0.001). Net reclassification improvement of standard CAC scores over DL CTAC scores was nonsignificant (-0.02; 95% CI: -0.11 to 0.07). The negative predictive values for MACE of zero CAC with standard (85%) and DL CTAC (83%) CAC scores were similar (P = 0.19). CONCLUSIONS: DL CTAC scores predict cardiovascular risk similarly to standard CAC scores quantified manually by experienced operators from dedicated electrocardiographically gated CAC scans and can be obtained almost instantly, with no changes to PET/CT scanning protocol.

Mitigating bias in deep learning for diagnosis of coronary artery disease from myocardial perfusion SPECT images.

PURPOSE: Artificial intelligence (AI) has high diagnostic accuracy for coronary artery disease (CAD) from myocardial perfusion imaging (MPI). However, when trained using high-risk populations (such as patients with correlating invasive testing), the disease probability can be overestimated due to selection bias. We evaluated different strategies for training AI models to improve the calibration (accurate estimate of disease probability), using external testing. METHODS: Deep learning was trained using 828 patients from 3 sites, with MPI and invasive angiography within 6 months. Perfusion was assessed using upright (U-TPD) and supine total perfusion deficit (S-TPD). AI training without data augmentation (model 1) was compared to training with augmentation (increased sampling) of patients without obstructive CAD (model 2), and patients without CAD and TPD < 2% (model 3). All models were tested in an external population of patients with invasive angiography within 6 months (n = 332) or low likelihood of CAD (n = 179). RESULTS: Model 3 achieved the best calibration (Brier score 0.104 vs 0.121, p < 0.01). Improvement in calibration was particularly evident in women (Brier score 0.084 vs 0.124, p < 0.01). In external testing (n = 511), the area under the receiver operating characteristic curve (AUC) was higher for model 3 (0.930), compared to U-TPD (AUC 0.897) and S-TPD (AUC 0.900, p < 0.01 for both). CONCLUSION: Training AI models with augmentation of low-risk patients can improve calibration of AI models developed to identify patients with CAD, allowing more accurate assignment of disease probability. This is particularly important in lower-risk populations and in women, where overestimation of disease probability could significantly influence down-stream patient management.

Current perspectives of ubiquitination and SUMOylation in abiotic stress tolerance in plants.

Post-translational modification (PTM) is a critical and rapid mechanism to regulate all the major cellular processes through the modification of diverse protein substrates. Substrate-specific covalent attachment of ubiquitin and Small Ubiquitin-Like Modifier (SUMO) with the target proteins, known as ubiquitination and SUMOylation, respectively, are crucial PTMs that regulate almost every process in the cell by modulating the stability and fidelity of the proteins. Ubiquitination and SUMOylation play a very significant role to provide tolerance to the plants in adverse environmental conditions by activating/deactivating the pre-existing proteins to a great extent. We reviewed the importance of ubiquitination and SUMOylation in plants, implicating its prospects in various abiotic stress regulations. An exhaustive study of molecular mechanisms of ubiquitination and SUMOylation of plant proteins and their role will contribute to the understanding of physiology underlying mitigation of the abiotic stresses and survival in plants. It will be helpful to strategize the improvement of crops for abiotic stress tolerance.

Deep Learning for Explainable Estimation of Mortality Risk From Myocardial Positron Emission Tomography Images.

BACKGROUND: We aim to develop an explainable deep learning (DL) network for the prediction of all-cause mortality directly from positron emission tomography myocardial perfusion imaging flow and perfusion polar map data and evaluate it using prospective testing. METHODS: A total of 4735 consecutive patients referred for stress and rest 82Rb positron emission tomography between 2010 and 2018 were followed up for all-cause mortality for 4.15 (2.24-6.3) years. DL network utilized polar maps of stress and rest perfusion, myocardial blood flow, myocardial flow reserve, and spill-over fraction combined with cardiac volumes, singular indices, and sex. Patients scanned from 2010 to 2016 were used for training and validation. The network was tested in a set of 1135 patients scanned from 2017 to 2018 to simulate prospective clinical implementation. RESULTS: In prospective testing, the area under the receiver operating characteristic curve for all-cause mortality prediction by DL (0.82 [95% CI, 0.77-0.86]) was higher than ischemia (0.60 [95% CI, 0.54-0.66]; P <0.001), myocardial flow reserve (0.70 [95% CI, 0.64-0.76], P <0.001) or a comprehensive logistic regression model (0.75 [95% CI, 0.69-0.80], P <0.05). The highest quartile of patients by DL had an annual all-cause mortality rate of 11.87% and had a 16.8 ([95% CI, 6.12%-46.3%]; P <0.001)-fold increase in the risk of death compared with the lowest quartile patients. DL showed a 21.6% overall reclassification improvement as compared with established measures of ischemia. CONCLUSIONS: The DL model trained directly on polar maps allows improved patient risk stratification in comparison with established methods for positron emission tomography flow or perfusion assessments.