Early Data Research Articles

Minimally Invasive Approach versus Sternotomy for Bentall Procedure: A Single-Center Experience

Background. The need for minimally invasive Bentall surgery for the treatment of aortic lesions with aortic insufficiency is increasing; however, comparative studies on the safety of the minimally invasive Bentall procedure and sternotomy Bentall procedure are lacking. Methods. Clinical data of 56 patients who underwent the Bentall procedure performed by the same surgical team at our center between December 2018 and December 2021 were retrospectively analyzed and followed up for 6 months after discharge. After dividing the patients into a right anterior chest minimally invasive Bentall surgery (RAT-Bentall) group (n = 13) and a conventional sternotomy Bentall surgery (C-Bentall) group (n = 43), intraoperative and early postoperative clinical data and echocardiography at 6 months after discharge were compared. Results. Compared with the C-Bentall group, the RAT-Bentall group had a lower postoperative visual analogue scale (VAS) pain score [(3.00 ± 2.08) VS (5.77 ± 1.84), P<0.001] and a shorter CSICU hospital stay [(1.90 ± 0.52) VS (2.51 ± 1.58) d, P<0.001] and postoperative hospital stay [(7.62 ± 1.81) VS (10.42 ± 2.45) d, P=0.035]. The incidence of postoperative complications and echocardiographic at 6-month follow-up after discharge was not statistically different between the two groups. Conclusion. The RAT-Bentall procedure is safe and effective. Compared with the sternotomy Bentall procedure, it can reduce postoperative pain as well as patients’ CSICU and postoperative hospital stay. Therefore, this technology is worth promoting and applying.

The JWST Resolved Stellar Populations Early Release Science Program. V. DOLPHOT Stellar Photometry for NIRCam and NIRISS

We present NIRCam and NIRISS modules for DOLPHOT, a widely used crowded-field stellar photometry package. We describe details of the modules including pixel masking, astrometric alignment, star finding, photometry, catalog creation, and artificial star tests. We tested these modules using NIRCam and NIRISS images of M92 (a Milky Way globular cluster), Draco II (an ultrafaint dwarf galaxy), and Wolf–Lundmark–Mellote (a star-forming dwarf galaxy). DOLPHOT’s photometry is highly precise, and the color–magnitude diagrams are deeper and have better definition than anticipated during original program design in 2017. The primary systematic uncertainties in DOLPHOT’s photometry arise from mismatches in the model and observed point-spread functions (PSFs) and aperture corrections, each contributing ≲0.01 mag to the photometric error budget. Version 1.2 of WebbPSF models, which include charge diffusion and interpixel capacitance effects, significantly reduced PSF-related uncertainties. We also observed minor (≲0.05 mag) chip-to-chip variations in NIRCam’s zero-points, which will be addressed by the JWST flux calibration program. Globular cluster observations are crucial for photometric calibration. Temporal variations in the photometry are generally ≲0.01 mag, although rare large misalignment events can introduce errors up to 0.08 mag. We provide recommended DOLPHOT parameters, guidelines for photometric reduction, and advice for improved observing strategies. Our Early Release Science DOLPHOT data products are available on MAST, complemented by comprehensive online documentation and tutorials for using DOLPHOT with JWST imaging data.

Linking preoperative and early intensive care unit data for prolonged intubation prediction.

Prolonged intubation (PI) is a frequently encountered severe complication among patients following cardiac surgery (CS). Solely concentrating on preoperative data, devoid of sufficient consideration for the ongoing impact of surgical, anesthetic, and cardiopulmonary bypass procedures on subsequent respiratory system function, could potentially compromise the predictive accuracy of disease prognosis. In response to this challenge, we formulated and externally validated an intelligible prediction model tailored for CS patients, leveraging both preoperative information and early intensive care unit (ICU) data to facilitate early prophylaxis for PI. We conducted a retrospective cohort study, analyzing adult patients who underwent CS and utilizing data from two publicly available ICU databases, namely, the Medical Information Mart for Intensive Care and the eICU Collaborative Research Database. PI was defined as necessitating intubation for over 24 h. The predictive model was constructed using multivariable logistic regression. External validation of the model's predictive performance was conducted, and the findings were elucidated through visualization techniques. The incidence rates of PI in the training, testing, and external validation cohorts were 11.8%, 12.1%, and 17.5%, respectively. We identified 11 predictive factors associated with PI following CS: plateau pressure [odds ratio (OR), 1.133; 95% confidence interval (CI), 1.111-1.157], lactate level (OR, 1.131; 95% CI, 1.067-1.2), Charlson Comorbidity Index (OR, 1.166; 95% CI, 1.115-1.219), Sequential Organ Failure Assessment score (OR, 1.096; 95% CI, 1.061-1.132), central venous pressure (OR, 1.052; 95% CI, 1.033-1.073), anion gap (OR, 1.075; 95% CI, 1.043-1.107), positive end-expiratory pressure (OR, 1.087; 95% CI, 1.047-1.129), vasopressor usage (OR, 1.521; 95% CI, 1.23-1.879), Visual Analog Scale score (OR, 0.928; 95% CI, 0.893-0.964), pH value (OR, 0.757; 95% CI, 0.629-0.913), and blood urea nitrogen level (OR, 1.011; 95% CI, 1.003-1.02). The model exhibited an area under the receiver operating characteristic curve (AUROC) of 0.853 (95% CI, 0.840-0.865) in the training cohort, 0.867 (95% CI, 0.853-0.882) in the testing cohort, and 0.704 (95% CI, 0.679-0.727) in the external validation cohort. Through multicenter internal and external validation, our model, which integrates early ICU data and preoperative information, exhibited outstanding discriminative capability. This integration allows for the accurate assessment of PI risk in the initial phases following CS, facilitating timely interventions to mitigate adverse outcomes.

Conditional Generative Adversarial Network for Early Classification of Longitudinal Datasets Using an Imputation Approach

Early classification of longitudinal data remains an active area of research today. The complexity of these datasets and the high rates of missing data caused by irregular sampling present data-level challenges for the Early Longitudinal Data Classification (ELDC) problem. Coupled with the algorithmic challenge of optimising the opposing objectives of early classification (i.e., earliness and accuracy), ELDC becomes a non-trivial task. Inspired by the generative power and utility of the Generative Adversarial Network (GAN), we propose a novel context-conditional, longitudinal early classifier GAN (LEC-GAN). This model utilises informative missingness, static features and earlier observations to improve the ELDC objective. It achieves this by incorporating ELDC as an auxiliary task within an imputation optimization process. Our experiments on several datasets demonstrate that LEC-GAN outperforms all relevant baselines in terms of F1 scores while increasing the earliness of prediction.

Treatment of unilateral olfactory neuroblastoma: Appropriate extent of surgical resection and potential for olfactory preservation.

Historically, comprehensive surgical resection for olfactory neuroblastoma has included the bilateral olfactory epithelium, cribriform plate, overlying dura, olfactory bulbs and tracts. This results in postoperative anosmia that may significantly impact a patient's quality of life without definitive added benefit in survival. The prevalence of occult intracranial disease is low, especially for Hyams grade I and II tumors. A unilateral approach sparing the contralateral cribriform plate and olfactory system can be considered for select cases of early stage, low-grade tumors when the disease does not cross midline to involve the contralateral olfactory cleft or septal mucosa and when midline dural margins can be cleared with frozen pathology. Approximately half of patients who undergo unilateral resection may have residual olfaction even with adjuvant unilateral radiation. Early data suggest favorable disease-free survival and overall survival for patients who underwent the unilateral approach; however, larger sample studies are needed to confirm comparability to bilateral resections regarding oncologic outcomes.

State of health prediction of lithium-ion batteries under early partial data based on IWOA-BiLSTM with single feature

The safe and stable operation of electric vehicles relies on fast and accurate predictions of the state of health (SOH) of the battery. To address challenges such as limited availability of extensive battery aging data or data with informative missingness, the novel SOH prediction method based on the improved method whale optimization algorithm (IWOA)-Bi-directional Long Short-Term Memory (BiLSTM) with strong correlated single aging feature is proposed. Firstly, to accurately predict the accelerated degradation process of the battery capacity, the knee-point in the capacity degradation curve is identified as a starting point for SOH prediction by Bacon-Watts model. Next, a small number of early partial aging features of the battery cycle are extracted, such as time of charging or discharging, and various correlation analysis methods are used to select the single feature with the highest correlation with capacity degradation to reduce the computational complexity of multiple feature factors. Finally, BiLSTM model is established to predict battery SOH. In addition, in order to improve the efficiency of the adjustment for hyperparameters, IWOA is proposed to optimize the BiLSTM’s hyperparameters. Compared to the traditional Whale Optimization Algorithm (WOA), IWOA has better global search capability, robustness, and efficiency through enhancements in search strategy, mutation operation, adaptive parameter adjustment, and performance optimization. The proposed method is validated using battery datasets from NASA and CALCE. Compared with BiLSTM and WOA-BiLSTM, the simulation results indicate that the MSE of SOH prediction based on IWOA-BiLSTM method mostly remains below 0.05, and index of agreement (IA) basically maintains higher than 99%.

Drug-resistant tuberculosis: a persistent global health concern.

Drug-resistant tuberculosis (TB) is estimated to cause 13% of all antimicrobial resistance-attributable deaths worldwide and is driven by both ongoing resistance acquisition and person-to-person transmission. Poor outcomes are exacerbated by late diagnosis and inadequate access to effective treatment. Advances in rapid molecular testing have recently improved the diagnosis of TB and drug resistance. Next-generation sequencing of Mycobacterium tuberculosis has increased our understanding of genetic resistance mechanisms and can now detect mutations associated with resistance phenotypes. All-oral, shorter drug regimens that can achieve high cure rates of drug-resistant TB within 6-9 months are now available and recommended but have yet to be scaled to global clinical use. Promising regimens for the prevention of drug-resistant TB among high-risk contacts are supported by early clinical trial data but final results are pending. A person-centred approach is crucial in managing drug-resistant TB to reduce the risk of poor treatment outcomes, side effects, stigma and mental health burden associated with the diagnosis. In this Review, we describe current surveillance of drug-resistant TB and the causes, risk factors and determinants of drug resistance as well as the stigma and mental health considerations associated with it. We discuss recent advances in diagnostics and drug-susceptibility testing and outline the progress in developing better treatment and preventive therapies.

Predicting battery lifetime under varying usage conditions from early aging data

Predicting battery lifetime under varying usage conditions from early aging data

Abstract 6870: Immunological aging in triple negative breast cancer leads to a chronic interferon phenotype and immune cell exhaustion that impedes response to therapy

Abstract Age is the biggest risk factor for developing most forms of cancer, and understanding how age impacts cancer progression is critical to improving responses to therapy. As we age immune function declines suggesting that therapeutic responses, particularly to immunotherapies, will also change with age. Early clinical trial data suggests that there are age-associated responses to immunotherapies. Clinical trials, however, are not designed to reflect patient chronological and immunological ages - over 50% of breast cancer patients are over the age of 60, yet less than 25% of patients in clinical trials are in this age range. Here, we utilize a preclinical mouse model of immunological aging to understand how aging impacts immune responses to triple-negative breast cancer (TNBC). Immunologically young (8 weeks old) and aged (~12 months old) FVB mice were orthotopically injected with syngeneic Met1 TNBC cells. We previously established that this preclinical model mimics human immunological aging and enables us to better understand how age impacts disease progression and therapeutic responses. Once tumors became palpable, mice were treated with paclitaxel or vehicle control and/or anti-PDL1 to mimic first-line therapeutic strategies. Tumors grew significantly faster in young control mice compared to aged, but young mice had a more robust response to therapy. Notably, only young mice had a significant benefit of combo therapy (paclitaxel + anti-PDL1) over monotherapy alone. Tumors in aged mice had significantly more infiltration of CD8+ cytotoxic T cells and FOXP3+ inhibitory T-regulatory cells compared to young control mice, yet only tumors in young mice exhibited an increase in T cell infiltration with chemo or immunotherapy. Depletion of either CD8+ or CD4+ T cells in immune-competent FVB mice both led to a complete loss of age-associated differences in tumor growth. RNA sequencing analysis indicated that the infiltrating T cells in aged mice may be exhausted due to a chronic interferon phenotype, as indicated by enrichments of interferon alpha, interferon gamma, and IL2 signaling pathways, as well as expression of RIPK1, TNFa, and the immune exhaustion markers PD1, CTLA4, TIM3, and LAG3. Analysis of published single cell RNAseq data indicated the same interferon response pathways enriched with age in human TNBC, particularly in the T cell compartment. Our findings show that the aging immune microenvironment significantly impacts tumor progression and that a chronic interferon phenotype with age may lead to T cell exhaustion and impaired therapeutic responses. This work suggests that targeting interferon signaling may lead to improved therapeutic responses. Together these findings demonstrate how immunological aging can impact disease progression and response to therapy and highlight the importance of developing age-stratified treatment strategies. Citation Format: Milos Spasic, Adrienne Parsons, Gregory Goreczny, Qiuchen Guo, Abigail Recko, Elizabeth Mittendorf, Peter van Galen, Sandra McAllister. Immunological aging in triple negative breast cancer leads to a chronic interferon phenotype and immune cell exhaustion that impedes response to therapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6870.

Abstract 2102: Understanding the mechanism of action of rigosertib in recessive dystrophic epidermolysis bullosa-associated squamous cell carcinoma through single cellanalysis

Abstract The allosteric inhibitor rigosertib (an experimental therapeutic) was previously identified as an effective inducer of G2M arrest and apoptosis in squamous cell carcinoma (SCC) keratinocytes isolated from patients with the rare genetic disease recessive dystrophic epidermolysis bullosa (RDEB), both in vitro and in vivo. Early clinical trial data treating RDEB-associated SCC with rigosertib has shown remarkable efficacy in a sub-set of patients and understanding the mechanism of action of rigosertib in this lethal cancer would provide rationale for patient stratification. Multiple mechanisms of action were previously attributed to rigosertib, including polo-like kinase-1 (PLK1), PI3-kinase, activated RAS and microtubule polymerization inhibition. Our published data comparing PLK1 siRNA with PI3-Kinase inhibitors, MAPK inhibitors and microtubule disruption favored PLK1 as a mechanism of action without definitive conclusion. We therefore sought to determine the mechanism of action using transcriptomic profiling. Flow cytometry experiments identified three main categories of time to increase G2M in nine separate RDEB SCC keratinocyte populations in culture ranging from 1 to 6 hours after exposure to 1uM rigosertib. Bulk-RNA sequencing of representative populations from time to G2M groupings failed to identify consistent transcriptional change after exposure to rigosertib. However, single cell sequencing was able to identify increases in sub-populations of cells associated with G2M as well as transient emergence of unique populations that were specific to individual patient populations after exposure to rigosertib in culture. Of note a transient population of RDEB SCC keratinocytes only present after 1 hour of exposure to rigosertib was characterized by increased expression of the potassium regulator KCNRG which has previously been associated with apoptosis in chronic lymphocytic leukemia. Ongoing work is determining the role this gene may play in the mechanism of apoptosis induction in RDEB SCC keratinocytes after exposure to rigosertib. Citation Format: Andrew P. South, Suhao Han, Paolo Fortina. Understanding the mechanism of action of rigosertib in recessive dystrophic epidermolysis bullosa-associated squamous cell carcinoma through single cellanalysis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2102.

Abstract 3849: Frontline ibrutinib-based therapy enhances CART cell functions in patients with CLL: Analysis of the phase 3 randomized clinical trial ECOG-ACRIN E1912

Abstract Introduction: Despite the remarkable activity of CD19-directed chimeric antigen receptor T cell therapy (CART19), durable remissions remain low, especially in chronic lymphocytic leukemia (CLL). Ibrutinib is an oral irreversible inhibitor of Bruton’s tyrosine kinase and is a first line therapy for CLL. Preclinical and early clinical data indicate that ibrutinib plus CART19 may be synergistic through inhibition of IL2-inducible T cell kinase. To systematically study the effects of prior ibrutinib treatment on subsequent CART functions, we utilized blood samples from the pivotal clinical trial E1912 (NCT02048813). E1912 was a phase 3 randomized trial comparing treatment with ibrutinib/rituximab (IR) to fludarabine/cyclophosphamide/rituximab (FCR) in previously untreated patients with CLL, which led to the FDA approval of ibrutinib as a first-line therapy in CLL. Methods: We evaluated the ex vivo expansion and effector functions of T cells and CART19 generated from patients (n=12) with CLL progression following treatment with IR or FCR. We generated CART19 from longitudinal samples obtained at baseline (pretreatment), 6 months post-treatment, 12 months post-treatment, and disease progression. Isolated T cells were lentivirally transduced to express CD28- or 41BB-costimulated CAR19. To assess antigen specific killing and proliferation, untransduced T cells or CART19 were co-cultured at a 1:1 ratio with the CD19+ cell line, JeKo-1, as a surrogate for CLL cells. Results: Ex vivo T cell expansion increased from an average of 1.5-fold at baseline to 6.7-fold at 6 months, 11.6-fold at 12 months, and 16.9-fold at progression, following ibrutinib-based therapy (p = 0.02, 0.04, and 0.04, respectively). Improved T cell expansion was independent of disease burden (p = 0.89, 0.73, and 0.54 for expansion vs B cell count at 6 months, 12 months, and progression). T cell expansion significantly worsened following treatment with FCR-based therapy compared to ibrutinib-based therapy (0.6- vs 11.6-fold at 12 months and 0.9- vs 16.9-fold at progression; p = 0.031 and 0.039, respectively). Starting at 6 months post-IR, CART19 demonstrated improved antitumor activity and antigen-specific proliferation against JeKo-1. There was no significant difference in the expansion of 41BB- vs CD28-costimulated CART19 following ibrutinib treatment. However, 41BB-CART19 showed enhanced antigen-specific proliferation upon stimulation with JeKo-1 (p = 0.008, 0.02, and 0.02 at 6 months, 12 months, and progression). Conclusion: Our results indicate significant improvement in T cell expansion and CART19 cell functions in patients with CLL following frontline ibrutinib-based therapy, and worsening T cell functions after FCR treatment. Improvement in T cell functions is independent of malignant B cell eradication and is more significant in 41BB-costimulated CART19. Citation Format: Truc Ngoc Huynh, Victoria Wang, Tait Shanafelt, Elizabeth L. Siegler, Carli M. Stewart, Kun Yun, Long K. Mai, Olivia Sirpilla, James H. Girsch, Claudia Manriquez-Roman, Jennifer M. Feigin, Makena Rodriguez, Brooke L. Kimball, Hong Xia, Reona L. Sakemura, Ismail Can, Omar L. Gutierrez-Ruiz, Ekene J. Ogbodo, Erin E. Tapper, Lionel A. Fonkoua, Mehrdad T. Hefazi, Michael W. Ruff, Sameer Parikh, Neil E. Kay, Saad J. Kenderian. Frontline ibrutinib-based therapy enhances CART cell functions in patients with CLL: Analysis of the phase 3 randomized clinical trial ECOG-ACRIN E1912 [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3849.

Abstract 5887: Identifying new immunotherapy targets using machine learning and ex vivo validation

Abstract Overview: Immunotherapy has revolutionized cancer treatment. However, existing immune checkpoint inhibitors (CPI) yield low response rates in most cancers, highlighting the need for new therapeutic options. Traditional immune-oncology (IO) target discovery relies on preclinical models, which struggle to recapitulate human tumor complexity, limiting translation potential. Attention is thus directed at harnessing multimodal patient molecular data with modern machine learning (ML) techniques to identify new IO targets which may have higher clinical viability. Approach: We posed IO target discovery as a binary classification task, training ML systems on known candidate drug targets that have progressed to stage I or higher clinical trials. We constructed a rich knowledge graph database to support model development. Graph nodes (n=11,919) comprised genes with edges linking genes involved in n=99,275 protein-protein interactions (PPIs; PMID: 28936969). Genes were labelled with n=6,387 gene-disease associations alongside bulk exome and transcriptome (RNAseq) features from n=1,317 CPI-treated patients (PMID: 33508232). We considered the role of different immune subsets by incorporating cell type-specific PPIs from single cell RNAseq atlases of n=350 samples (PMID: 31786210). To capture how antigen processing affects immunotherapy response, we also examined the immunopeptidome of n=60 patients (PMID: 30556813). Causal data on immune responses to genetic perturbation stemmed from n=7 publicly available CRISPR tumor-T cell co-cultures and n=15,442 SNP-phenotype links. Lastly, we used interpretability analysis to understand drivers of model predictions and elucidate critical genes that influence multiple IO target pathways. Results: Firstly, we developed an ensemble ML approach which achieved test ROC-AUC>0.75. Next, we used a graph-based ML framework which yielded superior performance (test ROC-AUC>0.90). Orthogonal validation confirmed these models can discriminate known targets by trial phase (p<0.001), predict patient response in new CPI trials (p<0.05), and identify genes that rank highly in unseen genome-wide CRISPR screens. Targets from both methods have entered experimental validation in patient-derived explants and organoid-immune co-cultures. Already, we have identified 2 novel targets predicted to relate to macrophage activity. Early data suggest that perturbing them leads to macrophage repolarization. Further validation experiments are ongoing. Conclusions: We will reveal candidate targets, showing that our method is effective at uncovering new IO targets and can identify critical nodes in biological networks that might be attractive hits. In addition, deciphering data types that drive model predictions provides a broader immunobiological understanding of anti-tumor immune responses. Thus, our results endorse using ML and multimodal data for novel IO target discovery. Citation Format: Marcellus Augustine, Nuno Rocha Nene, Krupa Thakkar, Danwen Qian, Evelyn Fitzsimons, Benjamin S. Simpson, Chris Watkins, Charles Swanton, Kevin Litchfield. Identifying new immunotherapy targets using machine learning and ex vivo validation [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5887.

S-PLUS: Photometric Recalibration with the Stellar Color Regression Method and an Improved Gaia XP Synthetic Photometry Method

We present a comprehensive recalibration of narrowband/medium-band and broadband photometry from the Southern Photometric Local Universe Survey (S-PLUS) by leveraging two approaches: an improved Gaia XP synthetic photometry (XPSP) method with corrected Gaia XP spectra, and the stellar color regression (SCR) method with corrected Gaia Early Data Release 3 photometric data and spectroscopic data from LAMOST Data Release 7. Through the use of millions of stars as standards per band, we demonstrate the existence of position-dependent systematic errors, up to 23 mmag for the main survey region, in the S-PLUS iDR4 photometric data. A comparison between the XPSP and SCR methods reveals minor differences in zero-point offsets, typically within the range of 1–6 mmag, indicating the accuracy of the recalibration, and a twofold to threefold improvement in the zero-point precision. During this process, we also verify and correct for systematic errors related to CCD position. The corrected S-PLUS iDR4 photometric data will provide a solid data foundation for conducting scientific research that relies on high-precision calibration. Our results underscore the power of the XPSP method in combination with the SCR method, showcasing their effectiveness in enhancing calibration precision for wide-field surveys when combined with Gaia photometry and XP spectra, to be applied for other S-PLUS subsurveys.

Estimation of Peanut Southern Blight Severity in Hyperspectral Data Using the Synthetic Minority Oversampling Technique and Fractional-Order Differentiation

Southern blight significantly impacts peanut yield, and its severity is exacerbated by high-temperature and high-humidity conditions. The mycelium attached to the plant’s interior quickly proliferates, contributing to the challenges of early detection and data acquisition. In recent years, the integration of machine learning and remote sensing data has become a common approach for disease monitoring. However, the poor quality and imbalance of data samples can significantly impact the performance of machine learning algorithms. This study employed the Synthetic Minority Oversampling Technique (SMOTE) algorithm to generate samples with varying severity levels. Additionally, it utilized Fractional-Order Differentiation (FOD) to enhance spectral information. The validation and testing of the 1D-CNN, SVM, and KNN models were conducted using experimental data from two different locations. In conclusion, our results indicate that the SMOTE-FOD-1D-CNN model enhances the ability to monitor the severity of peanut white mold disease (validation OA = 88.81%, Kappa = 0.85; testing OA = 82.76%, Kappa = 0.75).

Lifecourse correlates of self-rated health and associations with subsequent mortality: findings from the Hertfordshire Cohort Study

BackgroundPoor self-rated health (SRH) has been shown to predict adverse health outcomes among older people, however these associations have traditionally only been considered at one point in the lifecourse, usually midlife or later. Here we examined lifecourse correlates of SRH in early, mid and later life, relating these to subsequent risk of mortality in a community-dwelling cohort. Methods2989 men and women from the Hertfordshire Cohort Study (HCS) were included in this study. The HCS was initially retrospective and linked contemporary health outcome data to early life data available from health ledgers but investigations from baseline (1998–2004, aged 59–73) onwards have been prospective. At baseline, participants completed an initial clinic visit, which included questionnaire assessment of SRH, reported as 'excellent', 'very good', 'good', 'fair', or 'poor'. Socioeconomic, lifestyle, mental health and demographic information was also collected. Deaths were recorded from baseline to 31/12/2018. Baseline characteristics in relation to SRH were examined using sex-stratified ordinal logistic regression; these factors were examined in relation to mortality using sex-stratified Cox regression. Statistically significant exposures were then included in sex-stratified mutually-adjusted models. ResultsIn mutually-adjusted analysis, numerous contemporaneous correlates of poorer SRH in the seventh decade were identified and included obesity, lower physical activity, greater comorbidity and higher levels of depression among men and women. For example, odds ratios for being in a lower category of SRH were as follows: obese (BMI≥30) vs underweight/healthy (BMI<25) (men 1.60 (1.21, 2.11), women 1.65 (1.25, 2.17)) and per additional system medicated (men 1.62 (1.47, 1.77), women 1.53 (1.41, 1.66)). By contrast, factors earlier in the lifecourse (early growth, age left full-time education) were not associated with SRH in late adulthood. 36% of men and 26% of women died during follow-up. Hazard ratios (95% CI) for mortality per lower category of SRH were 1.22 (1.10,1.36) among men and 1.17 (1.01,1.35) among women after adjustment for age, BMI, smoking, physical activity, diet quality, education, home ownership status, comorbidity level and depression levels, suggesting residual confounding by other unrecorded factors that are related to SRH. ConclusionsPoorer SRH in the seventh decade was a risk factor for mortality. Importantly modifiable adverse health behaviours in the seventh decade, such as low physical activity, were associated with poorer SRH and later mortality after adjustment for socioeconomic factors and comorbidity level. By contrast early growth and education were not related to later SRH. These data suggest that attention to lifestyle in late midlife may be associated with better SRH and subsequent health outcomes, highlighting the value of intervention at this stage of the lifecourse.

Using early detection data to estimate the date of emergence of an epidemic outbreak.

While the first infection of an emerging disease is often unknown, information on early cases can be used to date it. In the context of the COVID-19 pandemic, previous studies have estimated dates of emergence (e.g., first human SARS-CoV-2 infection, emergence of the Alpha SARS-CoV-2 variant) using mainly genomic data. Another dating attempt used a stochastic population dynamics approach and the date of the first reported case. Here, we extend this approach to use a larger set of early reported cases to estimate the delay from first infection to the Nth case. We first validate our framework by running our model on simulated data. We then apply our model using data on Alpha variant infections in the UK, dating the first Alpha infection at (median) August 21, 2020 (95% interpercentile range across retained simulations (IPR): July 23-September 5, 2020). Next, we apply our model to data on COVID-19 cases with symptom onset before mid-January 2020. We date the first SARS-CoV-2 infection in Wuhan at (median) November 28, 2019 (95% IPR: November 2-December 9, 2019). Our results fall within ranges previously estimated by studies relying on genomic data. Our population dynamics-based modelling framework is generic and flexible, and thus can be applied to estimate the starting time of outbreaks in contexts other than COVID-19.

Endocardial Pulsed Field Ablation and the Oesophagus: Are Atrio-oesophageal Fistulas Now History?

Pulsed field ablation (PFA) is a novel energy form for the catheter ablation of cardiac arrhythmias, which uses electrical fields to mediate myocardial death via irreversible electroporation and other modalities. It is believed to offer relative tissue specificity, lowering - or even eliminating - the risk of complications associated with thermal energy, such as atrio-oesophageal fistulas. The proposed superior safety profile compared to thermal ablation has contributed to the enthusiastic implementation of PFA into clinical practice and is supported by early preclinical and clinical data. However, data about the effects of PFA on the oesophagus remain limited. This organ's susceptibility to PFA has important clinical ramifications and there are two highly relevant questions. First, is the oesophagus absolutely spared by PFA or is there susceptibility to injury at higher field strengths? Second, if oesophageal injury can occur, can atrio-oesophageal fistulas ensue? The aim of this article is to provide a literature review on the effects of PFA on the oesophagus and to address these questions based on the data described.

Enhancing seasonal influenza projections: A mechanistic metapopulation model for long-term scenario planning

In temperate regions, annual preparation by public health officials for seasonal influenza requires early-season long-term projections. These projections are different from short-term (e.g., 1–4 weeks ahead) forecasts that are typically updated weekly. Whereas short-term forecasts estimate what “will” likely happen in the near term, the goal of scenario projections is to guide long-term decision-making using “what if” scenarios. We developed a mechanistic metapopulation model and used it to provide long-term influenza projections to the Flu Scenario Modeling Hub. The scenarios differed in their assumptions about influenza vaccine effectiveness and prior immunity. The parameters of the model were inferred from early season hospitalization data and then simulated forward in time until June 3, 2023. We submitted two rounds of projections (mid-November and early December), with the second round being a repeat of the first with three more weeks of data (and consequently different model parameters). In this study, we describe the model, its calibration, and projections targets. The scenario projection outcomes for two rounds are compared with each other at state and national level reported daily hospitalizations. We show that although Rounds 2 and 3 were identical in definition, the addition of three weeks of data produced an improvement to model fits. These changes resulted in earlier projections for peak incidence, lower projections for peak magnitude and relatively small changes to cumulative projections. In both rounds, all four scenarios presented conceivable outcomes, with some scenarios agreeing well with observations. We discuss how to interpret this agreement, emphasizing that this does not imply that one scenario or another provides the ground truth. Our model's performance suggests that its underlying assumptions provided plausible bounds for what could happen during an influenza season following two seasons of low circulation. We suggest that such projections would provide actionable estimates for public health officials.

Transcriptome Analyses in Adult Olive Trees Indicate Acetaldehyde Release and Cyanide-Mediated Respiration Traits as Critical for Tolerance against Xylella fastidiosa and Suggest AOX Gene Family as Marker for Multiple-Resilience.

Xylella fastidiosa (Xf) is a global bacterial threat for a diversity of plants, including olive trees. However, current understanding of host responses upon Xf-infection is limited to allow early disease prediction, diagnosis, and sustainable strategies for breeding on plant tolerance. Recently, we identified a major complex trait for early de novo programming, named CoV-MAC-TED, by comparing early transcriptome data during plant cell survival with SARS-CoV-2-infected human cells. This trait linked ROS/RNS balancing during first hours of stress perception with increased aerobic fermentation connected to alpha-tubulin-based cell restructuration and control of cell cycle progression. Furthermore, our group had advanced concepts and strategies for breeding on plant holobionts. Here, we studied tolerance against Xf-infection by applying a CoV-MAC-TED-related gene set to (1) progress proof-of-principles, (2) highlight the importance of individual host responses for knowledge gain, (3) benefit sustainable production of Xf-threatened olive, (4) stimulate new thinking on principle roles of secondary metabolite synthesis and microbiota for system equilibration and, (5) advance functional marker development for resilience prediction including tolerance to Xf-infections. We performed hypothesis-driven complex analyses in an open access transcriptome of primary target xylem tissues of naturally Xf-infected olive trees of the Xf-tolerant cv. Leccino and the Xf-susceptible cv. Ogliarola. The results indicated that cyanide-mediated equilibration of oxygen-dependent respiration and carbon-stress alleviation by the help of increased glycolysis-driven aerobic fermentation paths and phenolic metabolism associate to tolerance against Xf. Furthermore, enhanced alternative oxidase (AOX) transcript levels through transcription Gleichschaltung linked to quinic acid synthesis appeared as promising trait for functional marker development. Moreover, the results support the idea that fungal endophytes strengthen Xf-susceptible genotypes, which lack efficient AOX functionality. Overall, this proof-of-principles approach supports the idea that efficient regulation of the multi-functional AOX gene family can assist selection on multiple-resilience, which integrates Xf-tolerance, and stimulates future validation across diverse systems.

Initial evaluation of the TALES (technology and literacy engagement after stroke) programme targeting functional literacy and social engagement in people with aphasia after stroke

ABSTRACT Background and aims Literacy skills are typically affected in people with stroke aphasia, leading to reduced engagement in functional everyday reading/writing tasks, such as email/messaging via social media. The aim of this study was to investigate the outcomes of the TALES (Technology And Literacy Engagement after Stroke) programme, a group-based online literacy-focused programme for people with aphasia after stroke. This first evaluation of this programme aimed to examine outcomes related to feasibility, changes in language, literacy and self-ratings of communication skills. Methods Five participants with aphasia were assessed on a range of outcome measures relating to language and self-perception of communication skills. These were: the Western Aphasia Battery-Revised (WAB-R; (Kertesz, 2007)), literacy subtests from the Psycholinguistic Assessment of Language Processing in Aphasia (PALPA; (Kay et al., 1996)) and the Communication Outcomes after Stroke (COAST; (Long et al., 2008)). TALES consisted of a weekly group online session, 1:1 supported telehealth reading/writing practice and regular messaging via email. Results and main contribution From a descriptive statistics perspective, the mean gains across the cohort were 7.94 (from 84.24 to 92.18) on the WAB AQ (aphasia quotient), 8.92 (from 81.18 to 90.10) on the WAB LQ (language quotient). Additionally, there was a change in the mean scores for PALPA subtests (31, 37 and 40) observed between the pre-treatment assessment and post-treatment assessment. Finally, the mean score increased by 8.8 (from 49.40 to 58.20) on the COAST (max score = 80). Results indicated gains in literacy and communication measures as well as changes to self-rating of communication reflecting changes in communication confidence. Conclusions Mindful of the threats to the internal validity inherent in the study design, the reported online therapy can overcome barriers such as distance and mobility limitations and may provide greater accessibility to therapy for individuals with aphasia. TALES was a low-therapist-input yet reasonably intensive treatment approach which embedded language practice immediately in functional use through the social group format and personally-relevant written communication. Implications The TALES programme meets many neuroscience principles of neurorehabilitation including “use it or lose it”, salience and transference. Future research may usefully investigate the contribution of the group format versus the functional literacy components towards these potentially promising early data.