Identification Of Phenotypes Research Articles

Spectral Fingerprinting Coupled Machine Learning: An Optimized Tool for Cellular Phenotype Identification

Spectral fingerprinting has emerged as a powerful tool for unraveling intricate interactions between the intracellular environment and nanomaterials. This study leverages spectral fingerprinting to deepen our comprehension of the interplay between macrophages and nanotubes, utilizing their Near Infrared fluorescence to discern cellular characteristics. This study employs Raman microscopy to showcase significant differences in defect ratio and intracellular processing of nanotubes, among macrophage phenotypes. The near infrared features of DNA-SWCNTs serve as distinctive markers for identifying different macrophage phenotypes. Here we use a support vector machine learning model to accurately identify M1 and M2 macrophages, achieving an impressive accuracy of 95%. The implications of this research extend to the development of nanomaterial-based platforms for cellular identification, holding promise for potential applications in real time in-vivo cell differentiation monitoring.This study underlines the pivotal role of nanotubes in delineating the diverse micro-environments of distinct macrophage phenotypes for clinical research. It emphasizes the potential of short DNA sequences and machine learning in characterizing and classifying cell phenotypes, offering valuable insights into the intersection of nanotechnology and biomedical research.This innovative approach holds great promise in advancing our understanding of dynamic cellular processes, disease states, disease progression, and response to stimuli in-vivo. The platform’s sensitivity to minute changes in the Near Infrared spectrum positions it as a valuable tool for researchers and clinicians. It facilitates the real time monitoring of cellular behavior within living systems, offering insights that extend to both fundamental research and clinical applications. The implications of this research pave the way for future developments in nanotechnology and biomedicine, bridging the gap between spectral analysis and cellular dynamics for enhanced diagnostic and therapeutic interventions. Figure 1

Phenotype identification and genome-wide association study of ear-internode vascular bundles in maize (Zea mays).

The vascular bundle in the ear-internode of maize is a key conduit for transporting photosynthetic materials between "source" and "sink", making it critically important to examine its micro-phenotypes and genetic architecture to identify advantageous characteristics and cultivate high-yielding and high-quality varieties. Unfortunately, the limited observation methods and scope of study precludes any comprehensive and systematic investigations into the microscopic phenotypes and genetic mechanisms of vascular bundle in maize ear-internode. In this study, 47 phenotypic traits were extracted in 495 maize inbred lines using micro computed tomography (Micro-CT) scanning technology and a deep learning-based phenotype acquisition method for stem vascular bundle, which included stem slice-related, epidermis zone-related, periphery zone-related, inner zone-related and vascular bundles-related traits. Phenotypic analysis indicated that there was extensive phenotypic variation of vascular bundle traits in ear-internode, especially that in the inner zone. Of these, 30 phenotypic traits with heritability greater than 0.70 were conducted for GWAS, and a total of 4,225 significant SNPs and 416 candidate genes with detailed functional annotations were identified. Furthermore, 20 genes were highly expressed in stem-related tissues, especially in maize internodes. Functional analysis of candidate genes indicated that the pathways obtained for candidate genes of different trait groups were distinct, mainly involved in vitamin synthesis and metabolism, transport of substances, carbohydrate derivative catabolic process, protein transport and localization, and anatomical structure development. The results of this study will help to further understand the phenotypic traits of stem vascular bundles and provide a reference for revealing the genetic mechanism of maize ear-internode vascular bundles.

Only repeatedly elevated IgG4 levels in primary sclerosing cholangitis may distinguish a particular patient phenotype

BackgroundPrimary sclerosing cholangitis (PSC) is a chronic liver disease leading to inflammation with scaring and strictures of bile ducts, which can lead to liver cirrhosis. A subtype of PSC characterized by high serum IgG4 (sIgG4) levels has been reported to be associated with poor outcomes, but the exact role and the longitudinal development of sIgG4 levels in PSC progression remains to be clarified. The aim of this study was to investigate if subsequent analysis of sIgG4 levels allows the identification of the PSC phenotype with high sIgG4.MethodssIgG4 values were repeatedly analysed in a well-characterized European PSC cohort of 110 individuals. Biochemical parameters, clinical endpoints, death and liver transplantation were compared between PSC subgroups.Results12.7% (n = 14) of PSC patients showed increased sIgG4 levels (PSC-IgG4). The values normalized in 57.1% (n = 8; PSC-IgG4norm) during follow-up measurements, whereas the values remained permanently elevated in 42.9% (n = 6; PSC-IgG4const). Serum values of AP and γGT were significantly higher in PSC-IgG4const compared to PSC-IgG4norm at final blood sampling. Furthermore, mean age at PSC diagnosis was markedly lower in PSC-IgG4const compared to PSC-IgG4norm.ConclusionsThis is the first study analyzing longitudinal development of sIgG4 in PSC. Our data indicate that only sequential determination of sIgG4 levels allow to accurately distinguish between the PSC phenotype with high sIgG4 and PSC with low sIgG4.Graphical abstract

Abstract Mo080: Clinically Variable Penetrant MYH7 G256E Mutation Shows Gene-Dose-Dependent HCM Disease Phenotype on a Transcriptomic, Proteomic, and Functional Level

Introduction: Hypertrophic cardiomyopathy (HCM) is a monogenic disorder caused by heterozygous (HET) mutations in sarcomere genes such as MYH7 . Recently, human-induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) carrying HET HCM mutations have shed new insights into the molecular mechanisms of HCM. However, the cause of phenotype variability and the role of allelic imbalance in phenotype penetrance of HET missense mutations such as MYH7 G256E remain unclear. Hypothesis: We hypothesize that hiPSC CMs carrying clinically variable penetrant HCM missense mutations such as MYH7 G256E show a gene dose-dependent disease phenotype indicating their susceptibility for allelic imbalance as a cause for variable disease phenotype penetrance. Methods: We generated HET and homozygous (HOM) hiPSC-lines for variable penetrant HCM mutation MYH7 G256E. We performed multiplexed single-cell RNA sequencing and quantitative proteomics analyses on wildtype (WT), HET, and HOM iPSC-CMs at day 30 of cardiac differentiation. Additional Digital Droplet PCR studies were performed to investigate the MYH7 mutant allele expression. The functional contractile phenotype was assessed with Sony SI8000 Cell motion imaging system. Results: We detected a large clone-to-clone and batch-to-batch variability in HET iPSC-CMs on a transcriptomic and proteomic level that obscured the identification of a consistent mutant phenotype in HET MYH7 G256E vs WT CMs. These phenotypes correlated with a 1:1 bulk ratio of mutant vs WT mRNA and protein expression, while overall MYH7 gene and protein expression remained comparable. Transcriptomic and proteomic analyses of WT, HET, and HOM CMs show a gene dose-dependent regulation of known hypertrophy response markers such as NPPB (mRNA: HET vs WT: +1.76x p .adj=7.0*10 -7 , HOM vs WT: +5.42x p.adj=1.5*10 -48 ; protein: HET vs WT: +1.15x, 95% CI [0.86, 1.58]; HOM vs WT: +1.51x, 95% CI [1.12, 2.01]) which functionally correlated with enhanced contractility (contraction velocity in μm/s: WT: 5.84 +/-2.24, HET: 6.04 +/-2.89, HOM: 7.77 +/-3.352, p=0.3*10 -2 ANOVA) and the more frequent occurrence of arrhythmias (WT: 1/46, HET: 0/55, HOM: 5/41). Conclusions: Hypertrophic disease phenotype in MYH7 G256E mutation iPSC-CMs shows a gene dose-dependent penetrance on a transcriptomic, proteomic, and functional level. This indicates susceptibility to allelic imbalance as a potential cause for variable disease penetrance.

Improved diagnosis of arrhythmogenic right ventricular cardiomyopathy using electrocardiographic deep learning

BackgroundArrhythmogenic right ventricular cardiomyopathy (ARVC) is a rare genetic heart disease associated with life-threatening ventricular arrhythmias. Diagnosis of ARVC is based on the 2010 Task Force Criteria (TFC), application of which often requires clinical expertise at specialized centers. ObjectiveThe purpose of this study was to develop and validate an electrocardiogram (ECG) deep learning (DL) tool for ARVC diagnosis. MethodsECGs of patients referred for ARVC evaluation were used to develop (n = 551 [80.1%]) and test (n = 137 [19.9%]) an ECG-DL model for prediction of TFC-defined ARVC diagnosis. The ARVC ECG-DL model was externally validated in a cohort of patients with pathogenic or likely pathogenic (P/LP) ARVC gene variants identified through the Geisinger MyCode Community Health Initiative (N = 167). ResultsOf 688 patients evaluated at Johns Hopkins Hospital (JHH) (57.3% male, mean age 40.2 years), 329 (47.8%) were diagnosed with ARVC. Although ARVC diagnosis made by referring cardiologist ECG interpretation was unreliable (c-statistic 0.53; confidence interval [CI] 0.52–0.53), ECG-DL discrimination in the hold-out testing cohort was excellent (0.87; 0.86–0.89) and compared favorably to that of ECG interpretation by an ARVC expert (0.85; 0.84–0.86). In the Geisinger cohort, prevalence of ARVC was lower (n = 17 [10.2%]), but ECG-DL–based identification of ARVC phenotype remained reliable (0.80; 0.77–0.83). Discrimination was further increased when ECG-DL predictions were combined with non–ECG-derived TFC in the JHH testing (c-statistic 0.940; 95% CI 0.933–0.948) and Geisinger validation (0.897; 95% CI 0.883–0.912) cohorts. ConclusionECG-DL augments diagnosis of ARVC to the level of an ARVC expert and can differentiate true ARVC diagnosis from phenotype-mimics and at-risk family members/genotype-positive individuals.

Phenotyping Refractory Cardiogenic Shock Patients Receiving Venous-Arterial Extracorporeal Membrane Oxygenation Using Machine Learning Algorithms.

This study used machine learning to categorize cardiogenic shock (CS) patients treated with venous-arterial extracorporeal membrane oxygenation (VA-ECMO) into distinct phenotypes. Subsequently, it aimed to clarify the wide mortality variance observed in refractory CS, attributing it to the condition's inherent heterogeneity. This study enrolled a cohort of CS patients who received VA-ECMO support. By employing rigorous machine learning (ML) techniques, we generated and validated clusters based on determinants identified through algorithmic analysis. These clusters, characterized by distinct clinical outcomes, facilitated the examination of clinical and laboratory profiles to enhance the understanding of patient responses to VA-ECMO treatment. In a study of 210 CS patients undergoing VA-ECMO treatment, 70.5% were male with a median age of 62, ranging from 53 to 67 years. Survival rates were 67.6% during VA-ECMO and 49.5% post-discharge. Patients were classified into three phenotypes based on the clinical and laboratory findings: "platelet preserved (I)", those with stable platelet counts, "hyperinflammatory (II)", those indicating significant inflammation, and "hepatic-renal (III)", those showing compromised liver and kidney functions. Mortality rates (25.0%, 52.8%, and 55.9% for phenotypes I, Ⅱ, and Ⅲ, respectively (p = 0.005)) varied significantly among these groups, highlighting the importance of phenotype identification in patient management. This study identified three distinct phenotypes among refractory CS patients treated using VA-ECMO, each with unique clinical characteristics and mortality risks. Thus, highlighting the importance of early detection and targeted intervention, these findings suggest that proactive management could improve outcomes for those showing critical signs.

Clinical phenotypes of sepsis: a narrative review.

Sepsis, characterized by an aberrant immune response to infection leading to acute organ dysfunction, impacts millions of individuals each year and carries a substantial risk of mortality, even with prompt care. Despite notable medical advancements, managing sepsis remains a formidable challenge for clinicians and researchers, with treatment options limited to antibiotics, fluid therapy, and organ-supportive measures. Given the heterogeneous nature of sepsis, the identification of distinct clinical phenotypes holds the promise of more precise therapy and enhanced patient care. In this review, we explore various phenotyping schemes applied to sepsis. We searched PubMed with the terms "Clinical phenotypes AND sepsis" for any type of article published in English up to September 2023. Only reports in English were included, editorials or articles lacking full text were excluded. A review of clinical phenotypes of sepsis is provided. While discerning clinical phenotypes may seem daunting, the application of artificial intelligence and machine learning techniques provides a viable approach to quantifying similarities among individuals within a sepsis population. These methods enable the differentiation of individuals into distinct phenotypes based on not only factors such as infectious diseases, infection sites, pathogens, body temperature changes and hemodynamics, but also conventional clinical data and molecular omics. The classification of sepsis holds immense significance in improving clinical cure rates, reducing mortality, and alleviating the economic burden associated with this condition.

Identification of severe acute pediatric asthma phenotypes using unsupervised machine learning.

More targeted management of severe acute pediatric asthma could improve clinical outcomes. To identify distinct clinical phenotypes of severe acute pediatric asthma using variables obtained in the first 12 h of hospitalization. We conducted a retrospective cohort study in a quaternary care children's hospital from 2014 to 2022. Encounters for children ages 2-18 years admitted to the hospital for asthma were included. We used consensus k means clustering with patient demographics, vital signs, diagnostics, and laboratory data obtained in the first 12 h of hospitalization. The study population included 683 encounters divided into derivation (80%) and validation (20%) sets, and two distinct clusters were identified. Compared to Cluster 1 in the derivation set, Cluster 2 encounters (177 [32%]) were older (11 years [8; 14] vs. 5 years [3; 8]; p < .01) and more commonly males (63% vs. 53%; p = .03) of Black race (51% vs. 40%; p = .03) with non-Hispanic ethnicity (96% vs. 84%; p < .01). Cluster 2 encounters had smaller improvements in vital signs at 12-h including percent change in heart rate (-1.7 [-11.7; 12.7] vs. -7.8 [-18.5; 1.7]; p < .01), and respiratory rate (0.0 [-20.0; 22.2] vs. -11.4 [-27.3; 9.0]; p < .01). Encounters in Cluster 2 had lower percentages of neutrophils (70.0 [55.0; 83.0] vs. 85.0 [77.0; 90.0]; p < .01) and higher percentages of lymphocytes (17.0 [8.0; 32.0] vs. 9.0 [5.3; 14.0]; p < .01). Cluster 2 encounters had higher rates of invasive mechanical ventilation (23% vs. 5%; p < .01), longer hospital length of stay (4.5 [2.6; 8.8] vs. 2.9 [2.0; 4.3]; p < .01), and a higher mortality rate (7.3% vs. 0.0%; p < .01). The predicted cluster assignments in the validation set shared the same ratio (~2:1), and many of the same characteristics. We identified two clinical phenotypes of severe acute pediatric asthma which exhibited distinct clinical features and outcomes.

Detecting sarcopenia in obesity: emerging new approaches.

Sarcopenic obesity is a likely common, but certainly underestimated obesity phenotype, with an important negative clinical impact. Its definition and diagnosis have however remained elusive until recently. Substantial progress has been recently made in sarcopenic obesity diagnostic tools, with the first international consensus proposed by the European Society for Clinical Nutrition and Metabolism (ESPEN) and the European Association for the Study of Obesity (EASO). Very encouraging results emerge from initial implementation of the ESPEN-EASO algorithm. In addition, even more recent progress in global consensus on sarcopenia conceptual definition is likely to further enhance consistency in sarcopenic obesity identification. The latter Global Leadership Initiative on Sarcopenia (GLIS) initiative also adopted a new definition of muscle specific strength. Its inclusion in sarcopenia diagnostic constructs opens the possibility of its potential evaluation in sarcopenic obesity, also considering the emerging positive impact of obesity treatment and fat loss on muscle functional parameters. New consensus tools for sarcopenic obesity diagnosis are likely to improve awareness, understanding, identification and treatment of this under-recognized obesity phenotype.

Identification and transcriptomic assessment of latent profile pediatric septic shock phenotypes

BackgroundSepsis poses a grave threat, especially among children, but treatments are limited owing to heterogeneity among patients. We sought to test the clinical and biological relevance of pediatric septic shock subclasses identified using reproducible approaches.MethodsWe performed latent profile analyses using clinical, laboratory, and biomarker data from a prospective multi-center pediatric septic shock observational cohort to derive phenotypes and trained a support vector machine model to assign phenotypes in an internal validation set. We established the clinical relevance of phenotypes and tested for their interaction with common sepsis treatments on patient outcomes. We conducted transcriptomic analyses to delineate phenotype-specific biology and inferred underlying cell subpopulations. Finally, we compared whether latent profile phenotypes overlapped with established gene-expression endotypes and compared survival among patients based on an integrated subclassification scheme.ResultsAmong 1071 pediatric septic shock patients requiring vasoactive support on day 1 included, we identified two phenotypes which we designated as Phenotype 1 (19.5%) and Phenotype 2 (80.5%). Membership in Phenotype 1 was associated with ~ fourfold adjusted odds of complicated course relative to Phenotype 2. Patients belonging to Phenotype 1 were characterized by relatively higher Angiopoietin-2/Tie-2 ratio, Angiopoietin-2, soluble thrombomodulin (sTM), interleukin 8 (IL-8), and intercellular adhesion molecule 1 (ICAM-1) and lower Tie-2 and Angiopoietin-1 concentrations compared to Phenotype 2. We did not identify significant interactions between phenotypes, common treatments, and clinical outcomes. Transcriptomic analysis revealed overexpression of genes implicated in the innate immune response and driven primarily by developing neutrophils among patients designated as Phenotype 1. There was no statistically significant overlap between established gene-expression endotypes, reflective of the host adaptive response, and the newly derived phenotypes, reflective of the host innate response including microvascular endothelial dysfunction. However, an integrated subclassification scheme demonstrated varying survival probabilities when comparing patient endophenotypes.ConclusionsOur research underscores the reproducibility of latent profile analyses to identify pediatric septic shock phenotypes with high prognostic relevance. Pending validation, an integrated subclassification scheme, reflective of the different facets of the host response, holds promise to inform targeted intervention among those critically ill.Graphical abstract

Perineural Invasion in Cervical Cancer: A Hidden Trail for Metastasis.

Perineural invasion (PNI), the neoplastic invasion of nerves, is an often overlooked pathological phenomenon in cervical cancer that is associated with poor clinical outcomes. The occurrence of PNI in cervical cancer patients has limited the promotion of Type C1 surgery. Preoperative prediction of the PNI can help identify suitable patients for Type C1 surgery. However, there is a lack of appropriate preoperative diagnostic methods for PNI, and its pathogenesis remains largely unknown. Here, we dissect the neural innervation of the cervix, analyze the molecular mechanisms underlying the occurrence of PNI, and explore suitable preoperative diagnostic methods for PNI to advance the identification and treatment of this ominous cancer phenotype.

Association of bronchial disease on CT imaging and clinical definitions of chronic bronchitis in a single-center COPD phenotyping study

IntroductionChronic Bronchitis (CB) represents a phenotype of chronic obstructive pulmonary disease (COPD). While several definitions have been used for diagnosis, the relationship between clinical definitions and radiologic assessment of bronchial disease (BD) has not been well studied. The aim of this study was to evaluate the relationship between three clinical definitions of CB and radiographic findings of BD in spirometry-defined COPD patients. MethodsA cross-sectional analysis was performed from a COPD phenotyping study. It was a prospective observational cohort. Participants had spirometry-defined COPD and available chest CT imaging. Comparison between CB definitions, Medical Research Council (CBMRC), St. George's Respiratory Questionnaire (CBSGRQ), COPD Assessment Test (CBCAT) and CT findings were performed using Cohen's Kappa, univariate and multivariate logistic regressions. ResultsOf 112 participants, 83 met inclusion criteria. Demographics included age of 70.1 ± 7.0 years old, predominantly male (59.0 %), 45.8 ± 30.8 pack-year history, 21.7 % actively smoking, and mean FEV1 61.5 ± 21.1 %. With MRC, SGRQ and CAT definitions, 22.9 %, 36.6 % and 28.0 % had CB, respectively. BD was more often present in CB compared to non-CB patients; however, it did not have a statistically significant relationship between any of the CB definitions. CBSGRQ had better agreement with radiographically assessed BD compared to the other two definitions. ConclusionIdentification of BD on CT was associated with the diagnoses of CB. However, agreement between imaging and definitions were not significant, suggesting radiologic findings of BD and criteria defining CB may not identify the same COPD phenotype. Research to standardize imaging and clinical methods is needed for more objective identification of COPD phenotypes.

The chromatin accessibility and transcriptomic landscape of the aging mice cochlea and the identification of potential functional super-enhancers in age-related hearing loss

BackgroundPresbycusis, also referred to as age-related hearing loss (ARHL), is a condition that results from the cumulative effects of aging on an individual's auditory capabilities. Given the limited understanding of epigenetic mechanisms in ARHL, our research focuses on alterations in chromatin-accessible regions.MethodsWe employed assay for transposase-accessible chromatin with high-throughput sequencing (ATAC-seq) in conjunction with unique identifier (UID) mRNA-seq between young and aging cochleae, and conducted integrated analysis as well as motif/TF-gene prediction. Additionally, the essential role of super-enhancers (SEs) in the development of ARHL was identified by comparative analysis to previous research. Meanwhile, an ARHL mouse model and an aging mimic hair cell (HC) model were established with a comprehensive identification of senescence phenotypes to access the role of SEs in ARHL progression.ResultsThe control cochlear tissue exhibited greater chromatin accessibility than cochlear tissue affected by ARHL. Furthermore, the levels of histone 3 lysine 27 acetylation were significantly depressed in both aging cochlea and aging mimic HEI-OC1 cells, highlighting the essential role of SEs in the development of ARHL. The potential senescence-associated super-enhancers (SASEs) of ARHL were identified, most of which exhibited decreased chromatin accessibility. The majority of genes related to the SASEs showed obvious decreases in mRNA expression level in aging HCs and was noticeably altered following treatment with JQ1 (a commonly used SE inhibitor).ConclusionThe chromatin accessibility in control cochlear tissue was higher than that in cochlear tissue affected by ARHL. Potential SEs involved in ARHL were identified, which might provide a basis for future therapeutics targeting SASEs related to ARHL.

Raman spectroscopic deep learning with signal aggregated representations for enhanced cell phenotype and signature identification.

Feature representation is critical for data learning, particularly in learning spectroscopic data. Machine learning (ML) and deep learning (DL) models learn Raman spectra for rapid, nondestructive, and label-free cell phenotype identification, which facilitate diagnostic, therapeutic, forensic, and microbiological applications. But these are challenged by high-dimensional, unordered, and low-sample spectroscopic data. Here, we introduced novel 2D image-like dual signal and component aggregated representations by restructuring Raman spectra and principal components, which enables spectroscopic DL for enhanced cell phenotype and signature identification. New ConvNet models DSCARNets significantly outperformed the state-of-the-art (SOTA) ML and DL models on six benchmark datasets, mostly with >2% improvement over the SOTA performance of 85-97% accuracies. DSCARNets also performed well on four additional datasets against SOTA models of extremely high performances (>98%) and two datasets without a published supervised phenotype classification model. Explainable DSCARNets identified Raman signatures consistent with experimental indications.

Abnormal blood pressure and pediatric obesity phenotype: Red flags for clinical practice

Background and aimssystolic and diastolic blood pressure values identify different subtypes of Primary Hypertension. In pediatric age, hypertension is often related to obesity. Characterization of an individual's Blood Pressure (BP) status needs multiple measurements in the course of time, but this is rarely the case, principally in everyday pediatrics. Thus, the finding of abnormal BP values even in a single routine check visit should need a special attention. The aim of this study was to evaluate if even a single abnormal measurement could be a marker of metabolic risk, according to clinical and/or metabolic phenotype and subtypes of hypertension in a population of overweight/obese children and adolescents. Methods and resultsWe reviewed data from 489 overweight-obese children and adolescents. BP values were classified according to American Academy of Pediatrics Guidelines (2017). Considering study design, we used the term “Abnormal” instead of “High” BP. Data on lipid profile, glucose metabolism and ultrasonographic pattern of the liver were recorded. Mean age was 9.4 ± 2.5 years: 76.9% had normal BP values. Children with Abnormal BP harbored more frequently an unfavorable metabolic profile. Children with Abnormal Systolic-Diastolic BP values had higher BMI z-score and Waist to Height ratio (p = 0.022 and p = 0.032). Hepatic Steatosis was detected in 4.5%, 14.8 % and 17.6 % of children with normal, abnormal systolic and abnormal systo-diastolic blood pressure, respectively. (p < 0.001). ConclusionIdentification of abnormal BP subtypes even in a single measurement could be useful for identification of specific clinical-metabolic phenotypes allowing an individual targeted management of obesity-related comorbidities.

Identification of disease phenotypes in acetylcholine receptor-antibody myasthenia gravis using proteomics-based consensus clustering

The clinical heterogeneity of myasthenia gravis (MG), an autoimmune disease defined by antibodies (Ab) directed against the postsynaptic membrane, constitutes a challenge for patient stratification and treatment decision making. Novel strategies are needed to classify patients based on their biological phenotypes aiming to improve patient selection and treatment outcomes. For this purpose, we assessed the serum proteome of a cohort of 140 patients with anti-acetylcholine receptor-Ab-positive MG and utilised consensus clustering as an unsupervised tool to assign patients to biological profiles. For in-depth analysis, we used immunogenomic sequencing to study the B cell repertoire of a subgroup of patients and an invitro assay using primary human muscle cells to interrogate serum-induced complement formation. This strategy identified four distinct patient phenotypes based on their proteomic patterns in their serum. Notably, one patient phenotype, here named PS3, was characterised by high disease severity and complement activation as defining features. Assessing a subgroup of patients, hyperexpanded antibody clones were present in the B cell repertoire of the PS3 group and effectively activated complement as compared to other patients. In line with their disease phenotype, PS3 patients were more likely to benefit from complement-inhibiting therapies. These findings were validated in a prospective cohort of 18 patients using a cell-based assay. Collectively, this study suggests proteomics-based clustering as a gateway to assign patients to a biological signature likely to benefit from complement inhibition and provides a stratification strategy for clinical practice. CN and CBS were supported by the Forschungskommission of the Medical Faculty of the Heinrich Heine University Düsseldorf. CN was supported by the Else Kröner-Fresenius-Stiftung (EKEA.38). CBS was supported by the Deutsche Forschungsgemeinschaft (DFG-German Research Foundation) with a Walter Benjamin fellowship (project 539363086). The project was supported by the Ministry of Culture and Science of North Rhine-Westphalia (MODS, "Profilbildung 2020" [grant no. PROFILNRW-2020-107-A]).

Leveraging GPT-4 for identifying cancer phenotypes in electronic health records: a performance comparison between GPT-4, GPT-3.5-turbo, Flan-T5, Llama-3-8B, and spaCy's rule-based and machine learning-based methods.

Accurately identifying clinical phenotypes from Electronic Health Records (EHRs) provides additional insights into patients' health, especially when such information is unavailable in structured data. This study evaluates the application of OpenAI's Generative Pre-trained Transformer (GPT)-4 model to identify clinical phenotypes from EHR text in non-small cell lung cancer (NSCLC) patients. The goal was to identify disease stages, treatments and progression utilizing GPT-4, and compare its performance against GPT-3.5-turbo, Flan-T5-xl, Flan-T5-xxl, Llama-3-8B, and 2 rule-based and machine learning-based methods, namely, scispaCy and medspaCy. Phenotypes such as initial cancer stage, initial treatment, evidence of cancer recurrence, and affected organs during recurrence were identified from 13646 clinical notes for 63 NSCLC patients from Washington University in St. Louis, Missouri. The performance of the GPT-4 model is evaluated against GPT-3.5-turbo, Flan-T5-xxl, Flan-T5-xl, Llama-3-8B, medspaCy, and scispaCy by comparing precision, recall, and micro-F1 scores. GPT-4 achieved higher F1 score, precision, and recall compared to Flan-T5-xl, Flan-T5-xxl, Llama-3-8B, medspaCy, and scispaCy's models. GPT-3.5-turbo performed similarly to that of GPT-4. GPT, Flan-T5, and Llama models were not constrained by explicit rule requirements for contextual pattern recognition. spaCy models relied on predefined patterns, leading to their suboptimal performance. GPT-4 improves clinical phenotype identification due to its robust pre-training and remarkable pattern recognition capability on the embedded tokens. It demonstrates data-driven effectiveness even with limited context in the input. While rule-based models remain useful for some tasks, GPT models offer improved contextual understanding of the text, and robust clinical phenotype extraction.

Evidence for neurobehavioral risk phenotypes at birth.

Observations of newborn behavior provide clinicians and researchers with a first description of the neurobehavioral organization of the newborn that is largely independent of the postnatal environment. The Neonatal Network Neurobehavioral Scale (NNNS) was developed in 2004 to evaluate how prenatal exposure to substances such as cocaine is related to neurobehavioral outcomes. There are now 156 empirical articles published using the NNNS, which we review and summarize. Z-scores from published studies using the NNNS were compiled and aggregated supporting the replicability of three newborn neurobehavioral phenotypes: one typical and two that are predictive of later cognitive and behavioral delay; hyper- and hypo-dysregulated newborns. These phenotypes emerged from independent samples and research groups and were identified in a variety of populations, including infants with prenatal substance exposure, preterm infants, and healthy term infants. Our findings show that newborn neurobehavior can be measured in a reliable and valid manner and that certain behavioral phenotypes, identifiable at birth, can predict neurodevelopmental challenges. These findings have important clinical utility. Intervening early with infants exhibiting these risk phenotypes may prevent later neurodevelopmental delay. IMPACT: We reviewed all empirical studies published using the Neonatal Network Neurobehavioral Scale and found evidence for two replicable stress phenotypes that predict later behavioral outcomes. This study highlights the utility of the Neonatal Network Neurobehavioral Scale for early identification of newborn neurodevelopmental risk phenotypes. Early identification of neurodevelopmental risk, when neuroplasticity is high, may ultimately reduce the burden of subsequent neurobehavioral problems through early intervention.

Stimulating macropinocytosis of peptide-drug conjugates through DNA-dependent protein kinase inhibition for treating KRAS-mutant cancer

KRAS-mutant cancers, due to their protein targeting complexity, present significant therapeutic hurdles. The identification of the macropinocytic phenotype in these cancers has emerged as a promising alternative therapeutic target. Our study introduces MPD1, an macropinocytosis-targeting peptide-drug conjugates (PDC), which is developed to treat KRAS mutant cancers. This PDC is specifically designed to trigger a positive feedback loop through its caspase-3 cleavable characteristic. However, we observe that this loop is hindered by DNA-PK mediated DNA damage repair processes in cancer cells. To counter this impediment, we employ AZD7648, a DNA-PK inhibitor. Interestingly, the combined treatment of MPD1 and AZD7648 resulted in a 100% complete response rate in KRAS-mutant xenograft model. We focus on the synergic mechanism of it. We discover that AZD7648 specifically enhances macropinocytosis in KRAS-mutant cancer cells. Further analysis uncovers a significant correlation between the increase in macropinocytosis and PI3K signaling, driven by AMPK pathways. Also, AZD7648 reinforces the positive feedback loop, leading to escalated apoptosis and enhanced payload accumulation within tumors. AZD7648 possesses broad applications in augmenting nano-sized drug delivery and preventing DNA repair resistance. The promising efficacy and evident synergy underscore the potential of combining MPD1 with AZD7648 as a strategy for treating KRAS-mutant cancers.

Identifying citrus self-incompatibility genotypes (S-genotypes) and discovering self-compatible mutants

Utilizing 32 previously identified S ribonuclease (S-RNase) gene sequences and abundant citrus resources, this study designed specific primers for 10 S-RNase genes. A total of 32 pairs of primers were used to analyze the self-incompatibility genotypes (S-genotypes) of 241 citrus resources, encompassing 105 mandarins, 47 pummelos, 69 oranges, and 20 lemons and citrons. These results provide theoretical guidance for parent selection in production and breeding programs. Among the 215 samples analyzed, two normal S-genotypes were identified, while no S-genotypes were detected in three samples. Notably, 21 samples, primarily citrons, exhibited amplification of only one S-genotype. Additionally, two pummelo samples showed amplification of three S-genotypes each. The integration of S-genotype and selfing phenotype identification revealed five newly discovered self-compatible mutated materials: Changsha ‘Shatian’ pummelo, large-fruited red pummelo, slender leaf ‘Mangshanyegan’, ‘Shatangju’, and W. Murcott. These findings provide valuable resources for investigating the self-compatibility mechanism in citrus.