This study aims to explore multiple profiles of second/foreign language (L2) readers by applying several mixture item response theory (MixIRT) models to the reading comprehension section of a high-stakes multiple-choice language test. The study characterizes the classes based on examinees' gender, lexico-grammatical knowledge, and overall language proficiency, measured by a Cloze test. Item responses of 2439 examinees to the reading comprehension section of the test were analyzed using a range of MixIRT models, including the mixture Rasch model, two parametric logistic MixIRT (2PL MixIRT), 3PL MixIRT, and 4PL MixIRT, with one to six latent classes. The 2PL IRT model with two classes showed the best fit to the data. The two classes were: (1) Local Processors and (2) Global Integrators. Class 1 comprises lower- to moderate-level proficiency examinees who possess restricted overall language proficiency and lexico-grammatical knowledge and rely on bottom-up, sentence-level processing, and superficial strategies such as memorizing isolated lexical and grammatical forms. However, Class 2 involves higher-proficiency examinees who have higher general language ability and lexico-grammatical knowledge and coordinate top-down and bottom-up processes, integrate higher- and lower-level (sub)skills, and adopt predictive and inferential strategies for coherently understanding a text.
Polygonum multiflorum Thunb. (P. multiflorum) is a traditional Chinese medicinal herb with a long history of use, and its polysaccharide constituents have been reported to possess a range of biological activities. Nevertheless, most existing studies have focused on crude polysaccharide fractions or preliminary bioactivity evaluations, leaving the fine structural features and molecular targets of these polysaccharides insufficiently defined. Here, a homogeneous polysaccharide, FM02 (Mw 12.1 kDa), was isolated from P. multiflorum. Structural characterisation indicated that FM02 is a novel arabinogalactoglucan with a backbone of alternating →4)-α-Glcp-(1→ and →4, 6)-α-Glcp-(1→ residues. Branches at the O-6 position of 1, 4, 6-linked glucosyl residues comprise three distinct types: a terminal T-α-Glcp cap, a trisaccharide branch →1)-α-Glcp-(4 → 1)-β-Galp-(3 → 1)-α-Araf, and a longer chain →1)-α-Glcp-(4→[1)-Galp-(4]3→[1)-α-Glcp-(4]4 → 1)-α-Glcp. Affinity pull-down of LX-2 hepatic stellate cell lysates combined with mass spectrometry identified YTHDF2 as a candidate binding protein. Surface plasmon resonance (SPR) detected a concentration-dependent interaction between FM02 and YTHDF2 (KD 5.47 × 10-7 M). Neither the acid-hydrolysis-resistant fraction FM02I nor the released degradation fragment FM02E showed detectable binding to YTHDF2, suggesting that the intact primary structure of the polysaccharide may be required to sustain this interaction. In summary, this study reports for the first time a novel arabinogalactoglucan, FM02, isolated from P. multiflorum, and reveals its potential direct interaction with the m6A reader protein YTHDF2, thereby providing foundational data for further activity screening, mechanistic studies, and related drug development of this polysaccharide.
Structural magnetic resonance imaging (MRI) is fundamental to presurgical localization in epilepsy, but subtle epilepsy-related abnormalities may not always be apparent on routine review. This study aimed to develop and externally validate an MRI-only graph attention transformer for ranking resection-related cortical candidates and to evaluate its reader-level utility. Graph Attention Transformer for Epilepsy-Related Candidate Zones (GATEZ) was developed using preoperative three-dimensional T1-weighted MRI from the publicly released IDEAS (Imaging Database for Epilepsy and Surgery) database. Participants with a 12-month International League Against Epilepsy class 1 outcome were split into training, validation, and internal test sets (n = 171/37/37). Each participant was represented as a 1000-parcel cortical graph with five regional morphometric features and an individualized Morphometric Inverse Divergence network; postoperative resection masks served as the surgical reference standard for model supervision. External validation used 183 consecutive surgical participants with epilepsy who underwent hybrid 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET)/MRI, with MRI only used for model inference. Reader-level utility was evaluated in a blinded three-reader study comparing MRI alone, MRI plus 18F-FDG PET, and MRI plus GATEZ. In the internal test cohort, GATEZ placed at least one resection-overlapping parcel within the Top-10 ranked candidates in 92% of participants, with a mean Top-10 positive predictive value of 62%. Performance remained stable in the independent external cohort, with an 87% Top-10 hit rate and 59% mean Top-10 positive predictive value. Node-level area under the precision-recall curve was .29 internally and .27 externally, indicating stable enrichment of resection-related regions among the highest ranked candidates. In the blinded reader study, MRI + GATEZ improved detection compared with MRI alone (74%-78% vs. 58%-66% across readers; adjusted p ≤ .001 for all readers) and performed similarly to MRI + FDG (78%-80% across readers; adjusted p ≥ .34 for all readers). GATEZ generates a concise Top-K shortlist of resection-related cortical candidates and may serve as a practical second-look aid for presurgical localization.
The gold standard for diagnosing central precocious puberty(CPP) is the gonadotropin-releasing hormone stimulation test, along with magnetic resonance imaging(MRI) of the brain and hypothalamus-pituitary region to rule out central organic causes. Recent advancements have led to a new medical imaging approach called radiomics. Our recent study showed that pituitary gland radiomics is a promising tool for diagnosing CPP. However, the role of the pineal gland in the onset of puberty has long been debated. Therefore, we investigated radiomic features of the pineal gland associated with puberty onset to identify changes that could assist physicians in the diagnostic workup of CPP. 45 girls with a confirmed diagnosis of CPP and 47 pre-pubertal, age-and sex-matched subjects(controls) were retrospectively enrolled. Two readers(R1, R2) with different levels of expertise in pediatric neuroradiology blindly segmented the pineal gland on MRI studies for radiomic features(RFs) calculation and manually evaluated the number and diameter of pineal cysts. Cross-validated linear discriminant analysis was used to develop, for each reader, both a radiomic model and a reference model based on pineal cyst features. Radiomics was evaluated in terms of predictive performances(ROC-AUC) and reliability of predictors between readers (intraclass correlation coefficient). Finally, the correlation between cysts' features and basal/peak gonadotropin and estradiol levels was also investigated. Two radiomic features were identified as the most predictive of CPP for both readers. However, these features were not the same for R1 and R2 readers and their values showed poor inter-reader reliability. Unpromising performance in the validation set was observed for pineal gland radiomics (ROC-AUC of 0.64 for R1 and 0.59 for R2). Similarly, the reference model based on pineal cyst features demonstrated a poor performance (ROC-AUC = 0.52, both readers). No significant correlations between cyst features and basal/peak gonadotropin levels were observed. Radiomic features of the pineal gland in girls did not show consistent and relevant changes with the onset of puberty and do not hold promise for the CPP diagnosis at variance with previous findings in the pituitary gland. Similarly, the number and size of cysts were not found to be specific for the onset of puberty.
To retrospectively evaluate the diagnostic performance and confidence of photon-counting computed tomography (PCCT) for detecting bone marrow edema (BME) in trauma patients, using magnetic resonance imaging (MRI) as the reference standard. In this exploratory, bone-region-level analysis, a selected pilot cohort of ten patients (mean age 56.2 years, 80% female) underwent both PCCT and MRI of peripheral joints within a 10-day interval. A total of 123 bone regions across the knee, pelvis/hip, wrist/hand, and elbow were analyzed. Two readers, blinded to clinical and MRI findings, independently assessed BME on PCCT using color-coded maps and standard images, recording diagnostic confidence on a 5-point Likert scale. MRI served as the reference for BME. Sensitivity, specificity, predictive values, accuracy, and interrater agreement (weighted Cohen's κ) were calculated. BME was present in all patients and in 28.5% (35/123) of bone regions on MRI. For PCCT, Reader 1 achieved a sensitivity of 0.63 (95% CI: 0.46-0.77), specificity of 0.97 (0.90-0.99), and accuracy of 0.87 (0.80-0.92) and Reader 2 achieved a sensitivity of 0.57 (0.41-0.72), specificity of 0.97 (0.91-0.99), and accuracy of 0.85 (0.78-0.91). Diagnostic specificity exceeded 0.93 across all joints, with the highest sensitivity in the knee. Inter-rater agreement was substantial (κ = 0.74). Both readers reported high diagnostic confidence. Reader 1 showed significantly higher confidence in correct classifications (p = 0.0017), whereas Reader 2 showed no significant difference between correct and incorrect classifications (p = 0.89). PCCT showed high specificity but only moderate sensitivity for BME detection at the bone-region level, particularly in large joints. These preliminary findings suggest that the high specificity of PCCT may support confirmation of BME in selected clinical scenarios when MRI is unavailable, contraindicated, or redundant. However, moderate sensitivity requires continued clinical vigilance and MRI confirmation for negative or equivocal findings. Larger validation studies are warranted. · PCCT shows high specificity for bone marrow edema detection. · Moderate sensitivity of PCCT limits standalone clinical use. · First multi-joint bone-region evaluation in a selected cohort of mainly trauma patients. · Substantial interrater agreement supports feasibility. · Positive PCCT findings are highly specific for BME; negative findings require MRI confirmation. · Shahzadi I, Reimann G, Schneider C et al. Exploratory Evaluation of Photon-Counting CT for Bone Marrow Edema Detection Across Multiple Joints: A Pilot Study. Rofo 2026; DOI 10.1055/a-2888-7989. Retrospektive Evaluation der diagnostischen Leistungsfähigkeit und der diagnostischen Sicherheit der Photon-Counting-Computertomografie (PCCT) zur Detektion von Knochenmarködemen (BME) bei Traumapatienten unter Verwendung der Magnetresonanztomografie (MRT) als Referenzstandard.In dieser explorativen Analyse auf Knochenregionsebene wurden zehn Patienten eines selektierten Pilotkollektivs (mittleres Alter 56,2 Jahre, 80% weiblich) eingeschlossen, bei denen sowohl eine PCCT als auch eine MRT peripherer Gelenke innerhalb eines Intervalls von 10 Tagen durchgeführt wurden. Insgesamt wurden 123 Knochenregionen aus Knie, Becken/Hüfte, Handgelenk/Hand und Ellenbogen analysiert. Zwei Untersucher, verblindet gegenüber klinischen und MRT-Befunden, beurteilten unabhängig voneinander das Vorliegen von BME in der PCCT anhand farbkodierter Karten sowie konventioneller Bildrekonstruktionen und dokumentierten die diagnostische Sicherheit auf einer 5-Punkte-Likert-Skala. Die MRT diente als Referenzstandard. Sensitivität, Spezifität, prädiktive Werte, Genauigkeit sowie die Interrater-Übereinstimmung (gewichtetes Cohen-κ) wurden berechnet.Ein Knochenmarködem war bei allen Patienten sowie in 28,5% (35/123) der Knochenregionen in der MRT nachweisbar. Für die PCCT erreichte Untersucher 1 eine Sensitivität von 0,63 (95%-KI: 0,46–0,77), eine Spezifität von 0,97 (0,90–0,99) und eine Genauigkeit von 0,87 (0,80–0,92); Untersucher 2 zeigte eine Sensitivität von 0,57 (0,41–0,72), eine Spezifität von 0,97 (0,91–0,99) und eine Genauigkeit von 0,85 (0,78–0,91). Die diagnostische Spezifität lag in allen untersuchten Gelenkregionen über 0,93, mit der höchsten Sensitivität im Kniegelenk. Die Interrater-Übereinstimmung war substanziell (κ = 0,74). Beide Untersucher berichteten eine hohe diagnostische Sicherheit. Untersucher 1 zeigte eine signifikant höhere diagnostische Sicherheit bei korrekten Klassifikationen (p = 0,0017), während Untersucher 2 keinen signifikanten Unterschied zwischen korrekten und inkorrekten Klassifikationen aufwies (p = 0,89).Die PCCT zeigte eine hohe Spezifität, jedoch nur eine moderate Sensitivität zur Detektion von Knochenmarködemen auf Knochenregionsebene, insbesondere in großen Gelenken. Diese vorläufigen Ergebnisse deuten darauf hin, dass die hohe Spezifität der PCCT die Bestätigung von BME in ausgewählten klinischen Szenarien unterstützen kann, wenn eine MRT nicht verfügbar, kontraindiziert oder nicht erforderlich ist. Die moderate Sensitivität erfordert jedoch weiterhin klinische Vigilanz sowie eine MRT-Bestätigung bei negativen oder äquivoken Befunden. Größere Validierungsstudien sind erforderlich. · Die PCCT zeigt eine hohe Spezifität bei der Detektion von Knochenmarködemen.. · Die moderate Sensitivität der PCCT limitiert den alleinigen klinischen Einsatz.. · Erste multigelenkige Analyse auf Knochenregionsebene in einem selektierten Kollektiv überwiegend traumatischer Patienten.. · Eine substanzielle Interrater-Übereinstimmung unterstreicht die Durchführbarkeit.. · Positive PCCT-Befunde sind hoch spezifisch für BME; negative Befunde erfordern eine MRT-Bestätigung..
To estimate an upper bound of memory-based re-identification risk for chest radiographs by testing radiologists under conditions that favor recognition. In this prospective, multicenter, web-based reader study, radiologists from 38 centers completed two reading phases. In Phase 1, each reader interpreted ten chest radiographs. After a minimum interval of 24 h, Phase 2 included six follow-up target examinations and six new non-target examinations (50% target prevalence). After each Phase-2 examination, readers indicated whether they remembered the patient. Following a positive response, they were asked separately whether they remembered the Phase-1 pseudonym and/or case position. Thirty-three readers with fully classifiable Phase-2 data contributed 396 Phase-2 examinations to the predefined primary analysis. Readers answered "remember" in 139 of 396 examinations (35.1%). Sensitivity for repeated target examinations was 50.0% (99/198), whereas 20.2% of new non-target examinations were nevertheless judged as remembered (40/198). Explicit identifiers were attempted in 23 of 396 examinations (5.8%). At least one explicit identifier, defined as the Phase-1 pseudonym and/or case position, was correct in five of 396 examinations (1.3%). In a low-prevalence model with one known patient per dataset, the positive predictive value of a "remember" response decreased from 2.44% in datasets of 100 radiographs to 0.25% in datasets of 1000 radiographs. Even in a design that favored memory, correct recall of explicit identifiers was rare, whereas false-positive recognition remained common. These findings support treating radiologists' memory as a limited, upper-bound component of re-identification risk, rather than assuming that familiarity routinely translates into identification. Even under conditions deliberately favoring memory, radiologists rarely converted familiarity with prior chest radiographs into correct explicit identifiers; in low-prevalence datasets, false-positive recognition dominated the practical meaning of a "remember" judgment. How often does a radiologist's feeling of familiarity with a previously seen chest radiograph translate into correct recall of a usable explicit identifier? In a deliberately memory-favoring design, sensitivity was 50.0%, but 20.2% of new examinations were false positives and only 1.3% yielded a correct explicit identifier.
Lysine β-hydroxybutyrylation (Kbhb) is a β-hydroxybutyrate-derived lysine acylation that connects ketone-body metabolism with chromatin regulation and non-histone protein function. Initially described as a fasting-responsive histone mark, Kbhb is now implicated in immune memory, metabolic adaptation, cancer metabolism and neuroprotection. This Review reframes Kbhb as a context-dependent metabolic acylation system. We discuss the metabolic origin of β-hydroxybutyryl-CoA, the writer, reader and eraser machinery of Kbhb, its crosstalk with acetylation, lactylation and crotonylation, and the evidence standards required to distinguish Kbhb-driven mechanisms from broader β-hydroxybutyrate biology. Recent studies have identified p300/CBP as a Kbhb writer, HDACs and sirtuins as erasers, and ENL as an H3K9bhb reader. Kbhb has also been linked to CD8+ T-cell memory, fasting-responsive chromatin remodeling, tumor metabolic rewiring and non-histone protein regulation. However, shared enzymes, overlapping acylation programs and pleiotropic BHB signaling complicate causal attribution. Kbhb should not be viewed as uniformly beneficial or pathological. Instead, its effects depend on donor availability, site specificity, reader engagement, tissue context, disease stage and competing acylations. Future work should prioritize site-resolved mass spectrometry, validated chromatin profiling, parallel acylome analysis, functional perturbation and clinically interpretable biomarkers to define which Kbhb events are causal and therapeutically actionable.
Development of varices is an important milestone in the natural history of patients with cirrhosis, and yet the data are sparse in terms of how best to assess for gastroesophageal varices as a clinical trial outcome in multicenter studies. Here we describe a centralized upper endoscopy (esophagogastroduodenoscopy [EGD]) reading process for assessing esophageal varices (EVs) and gastric varices (GVs) and to investigate inter-reader agreement between experienced endoscopists on the presence/size of varices. Patients with compensated metabolic dysfunction-associated steatohepatitis cirrhosis evaluated for inclusion in the NAVIGATE phase 2b/3 trial (NCT04365868) underwent EGD by local endoscopists, video recordings of which were centrally read by a pool of 6 qualified, trained reviewers. Two initial reviewers determined the presence/absence and size of varices, and in cases of disagreement, a third adjudicating reviewer assisted with the final determination. Agreement between the reviewers was analyzed using Cohen's kappa. Structured central blinded adjudication of varices was achieved at the participating centers across the globe. Each assigned reviewer completed their review within 24 hours of assignment. Of the 1006 EGDs reviewed, 216 (21.5%) had confirmed EVs, including 115 (53.2%) small, 71 (32.9%) medium, and 30 (13.9%) large varices. GVs were identified among 20 (2.0%) EGDs. Adjudication was required in 399 (39.7%) cases, with the third reviewer confirming varices in 216 (54.1%) cases. Percent agreement between reader pairs for EVs ranged from 40.0% to 100% (kappa 0.118-1.000), and inter-reader agreement for GVs varied between 81.8% and 100% (kappa 0.000-1.000). Centralized review of EGD video recordings, coupled with a structured adjudication process, can be implemented in large multicenter trials to provide reliable varices assessment in metabolic dysfunction-associated steatohepatitis clinical trials.
To determine if lesion conspicuity on contrast-enhanced mammography (CEM) is independently associated with malignancy. This retrospective, single-institution study identified consecutive abnormal screening and diagnostic CEMs between January 2019 and December 2021. The conspicuity of enhancing lesions on CEM recombined images was graded (low, moderate, or high) by one or both breast radiologists assigned as readers for the study, blinded to two-year imaging follow-up or biopsy pathology results. The positive predictive value (PPV) for malignancy was determined for each level of conspicuity. Across 476 CEM examinations in 455 women (median age, 49 years; IQR: 44-57), there were 563 enhancing lesions (55 malignant, 508 benign). Of the 563 enhancing lesions, 52% (292/563) were low conspicuity, 38% (213/563) moderate, and 10.3% (58/563) high. The PPV to predict cancer was 9% (95% CI: 6-12%), 11% (95% CI: 7-16%), and 10% (95% CI: 4-21%) for low, moderate, and high conspicuity (low vs high, p = 0.68; low vs moderate, p = 0.34; moderate vs high, p = 0.83). Of the 55 cancers, most high-conspicuity cancers presented on both recombined images and low-energy images (5/6; 83%), while most low- (21/25; 84%) and moderate-conspicuity cancers (17/24; 71%) presented on recombined images only (p = 0.006). Inter-reader agreement was almost perfect for 100 lesions graded by both breast radiologists (κ = 0.94). Lesion conspicuity is not independently associated with malignancy. Most enhancing lesions, including most cancers, show low or moderate conspicuity on CEM. Question Is lesion conspicuity on CEM, determined on the recombined images, an independent predictor of malignancy? Findings The PPV for low, moderate, and high conspicuity to predict malignancy was 8.6%, 11.3%, and 10.3%, respectively (non-significant differences between low, moderate, and high conspicuity). Clinical relevance Lesion conspicuity is not an independent predictor of malignancy. Enhancing lesions seen on recombined images should not be considered benign based on low conspicuity alone, as most low-conspicuity cancers presented with enhancement only and lacked a correlate finding on low-energy images.
Ventilation-Perfusion (V/Q) scan has been established as a nuclear imaging modality for diagnosis of pulmonary embolism (PE), especially in patients with contraindications computed tomography pulmonary angiography (CTPA). However, its use in United States is declining. We analyzed national Medicare data (2013-2023) to assess V/Q and Q scan utilization and physician procedure volumes. Our analysis shows that annual V/Q scans have been declining over the period from 2013 to 2023, with a marked drop occurring between 2019 and 2020 (COVID-19 era). Most of the V/Q scans have been read by low volume readers (< 11/ year). Low-volume readers percentage increased from 20.3% in 2013 to 43.4% in 2023. These trends may impact diagnostic quality and highlight the need to reassess training, competency, and access to V/Q imaging for patients contraindicated for CTPA.
Conventional CT (CCT) is widely used to assess hepatocellular carcinoma (HCC) after transarterial chemoembolization (TACE), but its diagnostic performance is often limited by lipiodol-induced beam-hardening artifacts and poor contrast resolution. Dual-energy CT (DECT) with low-keV monochromatic imaging may improve detection of viable residual tumors, yet its clinical value remains to be fully established. This study compared diagnostic performance, image quality, spatial accuracy, and interobserver agreement of DECT versus CCT for identifying viable HCC post-TACE using MRI as the reference standard. This retrospective, single-center study included 48 patients with 76 HCC lesions who underwent both DECT and MRI within 3 months after conventional TACE. Conventional CT (CCT) and 40-keV monoenergetic (MonoE40) images were reconstructed from DECT data. Diagnoses were independently assessed by radiologists blinded to MRI results. Signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), Dice similarity coefficient (DSC) for spatial agreement, and Fleiss' kappa for interobserver agreement were analyzed. Fifty-five lesions were viable per MRI. MonoE40 images showed significantly superior diagnostic performance compared to CCT (P < 0.05), especially for lesions with viable components < 2 cm (detection rates: 74.1 %-85.2 % vs. 25.9 %-48.1 %). MonoE40 also yielded higher diagnostic confidence, lesion conspicuity, and arterialphase CNR (P < 0.001). Dice coefficients for tumor delineation improved from 0.31 to 0.54 on CCT to 0.76-0.95 on MonoE40 (P < 0.05). Interobserver agreement at whole-lesion level was higher with MonoE40 (κ = 0.55) versus CCT (κ = 0.29), with the greatest improvement among less-experienced readers (κ from 0.35 to 0.59). DECT with 40-keV monochromatic reconstruction significantly improves detection of viable residual HCC after TACE, enhances tumor boundary delineation, and increases consistency among radiologists compared with CCT, especially benefiting less-experienced readers. These results support incorporating DECT into standard post-TACE imaging protocols.
Background Deep learning (DL) models have shown promise in diagnosing pancreatic cancer (PC); however, models that simultaneously detect both direct and indirect imaging findings associated with PC are lacking. Purpose To develop and evaluate DL models that detect direct and indirect imaging findings on noncontrast CT (NCCT) and contrast-enhanced CT (CECT) images for PC diagnosis. Materials and Methods This retrospective study from August 2007 to December 2022 included patients with PC and control patients. Two DL models were developed using NCCT and CECT to detect direct (pancreatic mass) and indirect (parenchymal atrophy, main pancreatic duct [MPD] dilatation, and MPD stenosis) imaging findings and diagnose PC based on these findings. For training and validation, CT scans from multiple institutions were manually annotated. Model evaluation was performed using two external test sets (CECT and NCCT sets). Receiver operating characteristic curve analysis was used to assess diagnostic performance. Model performance in detecting imaging findings and PC was compared with the performance of six physicians. The reference standard for PC diagnosis was histopathologic confirmation. Results This study included 2251 patients (mean age, 66 years ± 13.3 [SD]; age range, 20-96 years; 850 men). DL models demonstrated area under the receiver operating characteristic curve (AUC) values of 0.94, 0.90, 0.94, and 0.94 in the CECT set and 0.88, 0.88, 0.95, and 0.93 in the NCCT set for detecting pancreatic masses, parenchymal atrophy, MPD dilatation, and MPD stenosis, respectively. For PC diagnosis, DL models performed similarly to or better than the mean of six readers in the CECT (AUC, 0.99 vs 0.99; P = .84) and NCCT (AUC, 0.93 vs 0.91; P = .03) sets. For PCs that were 20 mm or smaller, the DL models demonstrated higher sensitivity than the reader mean in both the CECT (98% vs 82.6%; P < .001) and NCCT (86% vs 41.1%; P < .001) sets. Conclusion DL models detected direct and indirect imaging findings on CT images and diagnosed PC with performance comparable to or better than that of physicians, particularly for small PCs. © RSNA, 2026 Supplemental material is available for this article. See also the editorial by Bhayana and Rajpurkar in this issue.
N6-methyladenosine (m6A) regulatory genes are widespread in plants and play crucial roles in abiotic stress responses. However, these genes remain largely unexplored in kenaf (Hibiscus cannabinus L.), an economically important fiber crop. In this study, we conducted a genome-wide identification and comprehensive analysis of m6A regulatory genes in kenaf, uncovering 44 members, including 10 writers, 13 erasers, and 21 readers. These genes were unevenly distributed across 18 chromosomes. Through comprehensive analyses of collinearity, physicochemical properties, gene structure, and cis-acting elements in the promoter regions, we observed evolutionary conservation and enrichment of stress- and hormone-responsive elements among these genes. Notably, under abiotic stress and plant hormone treatment, m6A regulatory genes displayed distinct expression patterns, with the m6A readers gene HcYTH21 being strongly induced. We subsequently cloned HcYTH21 and generated its overexpression lines in both Arabidopsis and kenaf hairy roots. Overexpression of HcYTH21 enhanced salt tolerance in both Arabidopsis and kenaf, whereas virus-induced gene silencing (VIGS) of HcYTH21 significantly impaired salt tolerance. Under salt stress, HcYTH21-silenced plants showed decreased activities of antioxidant enzymes (SOD, CAT, and POD), downregulated expression of salt stress-related genes, and increased accumulation of H2O2 and O2 - accumulation, collectively contributing to reduced salt tolerance. Furthermore, HcYTH21 likely binds to the transcripts of positive salt-stress regulators, thereby stabilizing their mRNAs and promoting stress adaptation. This study first systematically analyzes the m6A regulatory gene family in kenaf and provides new insights into its roles in salt stress adaptation.
Many hospitals perform both axial fast spin-echo T2-weighted image (FSE T2WI) and axial gradient recalled echo T2-weighted (GRE) imaging. This study was performed to compare inter-reader agreement of cervical central spinal stenosis (CCSS) grading with the use of axial GRE imaging and axial FSE T2WI. We also compared the correlations between each radiologic grade and clinical manifestations. A total of 143 patients (M: F = 71:72; mean age, 52 years) who underwent magnetic resonance imaging of the cervical spine at our hospital were included. Two radiologists evaluated the degree of CCSS from the level of C2-3 to the level of C6-7 using axial GRE imaging, axial FSE T2WI, axial GRE imaging with sagittal T2WI, and axial FSE T2WI with sagittal T2WI. Kappa statistics were used to analyze the inter-reader agreement. The existence of substantial agreement between GRE axial and FSE T2 axial images was reported by both readers (0.657 ≤ κ ≤ 0.665), who also reported almost-perfect agreement between GRE axial + T2 sagittal and T2 axial + T2 sagittal imaging (0.958 ≤ κ ≤ 0.979). The GRE axial, GRE axial + T2 sagittal, and T2 axial + T2 sagittal images showed superior correlation (moderate) compared to that of T2 axial images only (weak) in revealing the correlation between Kang grade and clinical manifestation. The agreement of CCSS grading with axial GRE imaging and axial FSE T2WI findings was substantial. Using axial GRE images led to a superior correlation between magnetic resonance sequence and clinical manifestations relative to using axial FSE T2WI.
Accurate aortic segmentation on computed tomography angiography (CTA) is essential for diagnosing aortic disease, cardiovascular risk assessment, and surgical planning. Deep learning algorithms, such as TotalSegmentator-AI, offer fully automated multi-organ segmentation, yet their performance in pathological aortic conditions remains uncertain. This study performs a clinical stress-test of TotalSegmentator-AI, mapping its boundaries and structural failure modes across a spectrum of normal and pathological cases. In this monocentric, retrospective study, 60 CTA scans from 2014 to 2024 were categorized into six groups: young, elderly, aneurysm, dissection, venous phase, and non-contrast phase. TotalSegmentator-AI was applied without manual correction. Two radiologists independently rated six aortic segments per scan using a five-point qualitative scale. Quantitative segmentation errors were correlated with qualitative scores using Spearman's correlation, and inter-reader agreement was assessed with weighted Cohen's kappa. All scans were successfully processed, yielding 360 aortic segments. Median segmentation quality was 4 [IQR 4-5], with 77% rated good or excellent. Performance was consistent across segments (p = 0.16) but varied by category (p < 0.001): best in young patients (5 [IQR 4-5]) and adequate in non-contrast and venous-phase scans (4 [IQR 4-5]), poorest in dissections (3 [IQR 3-4]) and aneurysms (4 [IQR 3-4]). A strong negative correlation was observed between qualitative scores and quantitative errors (ρ = -1, p = 0.017). Inter-reader agreement was substantial (κ = 0.72). TotalSegmentator-AI achieves accurate aortic segmentation in normal anatomy but is inadequate for unsupervised clinical use in complex pathologies like aneurysms and dissections. Comprehensive human-in-the-loop quality control or dedicated pathology-inclusive models are mandatory before AI-based segmentation can be safely integrated into vascular clinical workflows.
Small extracellular vesicles (sEVs) are critical mediators of tumor microenvironment communication, largely through the selective transfer of microRNAs (miRNAs) that reprogram recipient cells. Active miRNA sorting into sEVs depends on RNA‑binding proteins (RBPs), sequence determinants, and RNA modifications. Here, a functional interplay between the RBP SYNCRIP and N6‑methyladenosine (m6A) RNA methylation controlling miRNA loading into hepatocellular carcinoma (HCC)‑derived sEVs has been disclosed. It is reported that (i) METTL3 (Methyltransferase-like-3)‑dependent m6A modification is required for efficient binding of SYNCRIP to specific miRNAs, thereby enabling their selective incorporation into sEVs; (ii) silencing of SYNCRIP markedly reshapes the sEV miRNA-cargo and impairs the ability of HCC‑derived sEVs to induce epithelial-to-mesenchymal transition (EMT) in non‑tumorigenic hepatocytes. Notably, (iii) depletion of METTL3 produces an even stronger effect, indicating that m6A methylation represents an upstream and essential determinant of SYNCRIP‑mediated miRNA export. Mechanistically, the data identify SYNCRIP as an m6A‑dependent miRNA reader, adding epitranscriptomic regulation to sequence‑based miRNA sorting into sEVs. Functionally, disruption of this interaction attenuates sEV‑driven EMT and pro‑tumorigenic signaling. Collectively, these findings uncover a novel regulatory axis governing sEV miRNA cargo selection and highlight the m6A-SYNCRIP interplay as a potential therapeutic target to interfere with sEV‑mediated tumor progression and metastasis.
Reports the notice of retraction of "Dehumanization without antipathy: Subtle and blatant measures reveal a shared regulatory function" by Katrina M. Fincher, Asteya Percaya and Starlett Hartley (Journal of Experimental Psychology: General, Advanced Online Publication, Oct 27, 2025, np; see record 2026-79271-001). A reader reported an error in the paper's reference list. Further inspection identified nine references with multiple other errors. In addition, errors in data processing and reporting were identified that affected the structure of the data and the resulting analyses, leading to changes in parameter estimates and the statistical significance of several reported effects. The article authors were made aware of these issues and confirmed errors in data analysis and in the reporting of the results as well as non-disclosed possible use of artificial intelligence tools in the generation of the reference list. The authors revised the analyses and updated the data and code that was posted to the online repository and drafted corrections to the paper. After reviewing the corrections, the Editor determined that the nature and scope of errors in the published paper were beyond what could be addressed with a correction notice. (The following abstract of the original article appeared in record 2026-79271-001.) Dehumanization, the perception of others as less than fully human, is widely invoked in discourse on ethnopolitical conflict. Yet its validity as a psychological construct has come under growing scrutiny. Critics have questioned its divergent validity, arguing it may merely reflect interpersonal and intergroup bias, and its convergent validity, given the proliferation of diverse and potentially unrelated measures. The present research speaks to both concerns by leveraging the context of contagious disease, which introduces motivational conflict between recognizing others' humanity and managing personal risk. Because contagious disease threatens friends and family as much as strangers, this context provides a stringent test of whether dehumanization operates independently of prejudice. It also enables a test of functional convergence: whether diverse dehumanization measures respond in parallel to a shared motivational input. Findings from six studies (N = 5,253) assessing four common operationalizations-blatant dehumanization, animalization, mechanization, and mind denial-support the construct's distinctiveness and its coherence. Contagion cues reliably elicited dehumanization, and this effect was not moderated by relational closeness: Perceived disease risk increased dehumanization equally for friends and family. Findings also support the construct's coherence: All four measures responded similarly to disease threat. Multilevel models treating the measure as a random effect revealed substantial shared variance across operationalizations. Together, these findings support the distinctiveness and coherence of psychological conceptions of dehumanization as a flexible regulatory mechanism. (PsycInfo Database Record (c) 2026 APA, all rights reserved).
Falls remain a leading cause of injury-related morbidity and mortality among adults aged 65 and older, and these human and economic costs keep rising as populations age worldwide. Most existing risk assessment tools rely on periodic clinical evaluations or single-sensor wearable devices, neither of which tracks the dynamic, multifactorial nature of fall risk throughout daily life. In this paper we describe the design, implementation, and pilot-level field validation of an integrated system that pairs a multi-sensor wearable unit (triaxial accelerometer, gyroscope, insole pressure array, and wrist-worn photoplethysmography) with a CNN-LSTM deep learning backbone enhanced by eight-head attention. The network ingests temporally aligned signals from all four modalities, extracts hierarchical spatiotemporal features, and outputs a continuous risk score that a rule-based engine maps onto four graded alert tiers. We collected data from 120 community-dwelling older adults (mean age 72.4 years) over six months and evaluated the system on a held-out test set of 18 participants (12,540 windows, 6 fall events). The model reached 94.2% overall accuracy and an AUC of 0.967, surpassing random forest, SVM, single-modality CNN and LSTM, a Transformer encoder baseline, and a late-fusion variant. We stress, however, that the extreme class imbalance (fall windows constitute only 0.05% of total windows, with a high-risk PPV of merely 6.25%) severely limits the stability and interpretability of sensitivity estimates; a single misclassification among the six fall events shifts sensitivity by roughly 17% points. End-to-end pipeline latency averaged 138 ms on a smartphone, satisfying real-time operational requirements. In a subsequent three-month deployment with 80 participants, the group receiving active alerts recorded 62% fewer falls than the passive-monitoring control group (0.82 vs. 2.14 falls per person-month; p < 0.01). This deployment, however, was conceived as a technical field test of system operability rather than a clinical efficacy trial; the fall incidence difference is an exploratory, secondary observation confounded by concurrent daily risk summaries and caregiver notifications, and it should not be interpreted as evidence of causal preventive effect. Importantly, the window-level classification PPV (6.25%) and the deployment-level alert PPV (87.8%) differ because the latter applies episode-level aggregation through the rule-based warning engine, and readers should note this distinction when comparing metrics across sections. User satisfaction averaged 4.3/5 on a Likert scale, and wearing compliance reached 91.3% of waking hours. Taken together, these results suggest that multimodal sensor fusion combined with attention-based deep learning offers a technically viable path toward continuous, community-based fall risk monitoring, while also underscoring the need for larger-scale, prospectively registered randomized controlled trials to establish whether such systems can causally reduce fall incidence.
Zygotes are persisting organisms. That is, zygotes are organisms and most born human beings are identical to the zygotes from which they originated. I defend these claims against recent critiques. Chunghyoung Lee, for example, argues that for any zygote, z, z may develop into one of several, numerically distinct infants. If so, then for any infant, that infant is not identical to the zygote from which he or she originated. If Lee is correct, then zygotes are like gametes, which may give rise to mature human beings, but cease to exist along the way. This, Lee claims, suggests that zygotes are not organisms. I respond that Lee (like many others) confuses zygotic parts with whole zygotes. The error lies in identifying zygotes (and embryos) with their inner contents alone. Zygotes are more than their internal parts, however, just as the reader is more than their heart, lungs, and kidneys. To advance these claims, I defend a version of "zona-essentialism," which maintains that the zona pellucida-the membrane surrounding zygotes and early embryos-is essential to their identity during early stages of life. By distinguishing between zygotic parts and whole zygotes, I show that Lee's (and others') arguments fail to establish that zygotes are not organisms. I conclude by discussing practical implications for finding that zygotes are human organisms. Clinicians and researchers must embrace reality: individual human beings are routinely killed in the name of scientific advancement and reproductive autonomy. Failure to acknowledge this-e.g., by using euphemisms to obscure the truth-is problematic.
Response Evaluation Criteria in Solid Tumors (RECIST 1.1) is currently the standard for tumor response measurement, but it is time-consuming, labor-intensive, and subject to reader variability. Tumor growth inhibition (TGI) models built on RECIST datasets often consider the time-course of all lesions as one aggregated tumor, as represented by the sum of longest diameters. An AI-based tool was utilized to obtain comprehensive data of both aggregated tumor and individual lesion growth dynamics in terms of their longest diameters and 3D volumes. Standard and AI-derived measurements were used to develop TGI models to evaluate the time-course of aggregate and lesion-level tumor shrinkage for patients receiving lorlatinib in the phase III CROWN trial. Additionally, a parametric time-to-event model was developed to describe the PFS probability. Lesion-level TGI models were connected to the PFS model by investigating the influence of various TGI-derived tumor metrics and parameter estimates on the association with PFS. TGI models developed using the more comprehensive AI-generated lesion measurements demonstrated better precision and were more robust than traditional RECIST-based models when tested under different scenarios using stochastic simulation and estimation (SSE). The mean value of tumor decay rate (KD) and the mean lesion size at week 8 were found to be significantly associated with PFS from the lesion-level models. In contrast, aggregated TGI models did not identify any significant prognostic factors of PFS. This modeling approach, utilizing AI-powered auto-measurement and incorporating individual lesion dynamics, provides a more complete understanding of tumor growth and can enable early decision-making for lesion-targeting therapies.