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A capacitive photodiode for analogue and digital processing

Diagnostic accuracy of automated hematology analyzer abnormal flags for detecting hematological malignancies: A systematic review and meta-analysis

Background Hematological malignancies including leukemia, lymphoma, and myelodysplastic syndromes, are characterized by clonal proliferation of abnormal blood or bone marrow cells. Early and accurate detection is essential for improving treatment outcomes and survival. Automated hematology analyzers generate abnormal flags that may indicate underlying hematologic malignancies; however, their overall diagnostic accuracy has not been comprehensively evaluated. This systematic review and meta-analysis aimed to assess the diagnostic performance of abnormal flags for detecting hematological malignancies. Methods A systematic search of PubMed, PubMed Central, Scopus, ScienceDirect, and Google Scholar was conducted to identify relevant diagnostic accuracy studies. Methodological quality was evaluated using the Quality Assessment of Diagnostic Accuracy Studies-2(QUADAS-2) tool. Pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio were calculated using a bivariate random-effects model in Stata version 17.0. Heterogeneity was assessed using the I 2 statistic, and subgroup and meta-regression analyses were performed to explore potential sources of variability. Results Twenty-eight studies met the inclusion criteria. The pooled sensitivity and specificity of abnormal hematology analyzer flags for detecting hematological malignancies were 91% (95% CI: 87%–94%) and 89% (95% CI: 84%–92%), respectively, indicating good diagnostic accuracy. Significant heterogeneity was observed across studies (I 2  > 50%). Meta-regression analysis identified the type of abnormal flag as a significant source of heterogeneity in sensitivity (p < 0.001), whereas both the type of abnormal flag and the analyzer platform significantly influenced specificity. Conclusion Automated hematology analyzer abnormal flags showed promising diagnostic performance. However, substantial heterogeneity and differences in analyzer platforms, flag types, and reference standards reduce the certainty and generalizability of pooled estimates. Nevertheless, these findings support the use of abnormal hematology analyzer flags as an effective initial screening tool in routine laboratory practice, particularly in resource-limited settings where rapid and cost-effective diagnostic support is essential. Systematic review registration PROSPERO (CRD42024601908 ).

Experimental investigation of internal gas distribution characteristics in loaded water-bearing coal during N2 displacement

Accurate, transferable, and verifiable machine-learned interatomic potentials for layered materials

Integrative multi-omics and machine learning identify CHRNA1 putative circadian-immune hub in COPD

Background Circadian rhythm disruption is increasingly recognized as a contributor to chronic inflammatory disorders; however, its specific significance and underlying mechanisms in chronic obstructive pulmonary disease (COPD) remain unclear. This study aimed to identify circadian rhythm-associated biomarkers in COPD and explore their diagnostic value, immune correlations, and therapeutic potential. Methods This study integrated four lung transcriptomic datasets from the public GEO database (GSE151052, GSE38974, and GSE76925 as the discovery set, and GSE47460 as the validation set). Differentially expressed circadian rhythm‑related genes (DECRRGs) were identified by intersecting differentially expressed genes with circadian rhythm‑related genes. Functional enrichment analyses (GO and KEGG) were performed, and three machine learning algorithms were applied to screen for signature DECRRGs. An exploratory risk stratification model based on multivariate logistic regression was constructed and evaluated. Immune cell infiltration was assessed using CIBERSORT, and single-cell RNA sequencing analysis was conducted to localize the distribution of key circadian rhythm genes within specific lung cell populations. Finally, the expression of a core gene CHRNA1 was validated by qRT-PCR in peripheral blood samples from COPD patients and healthy controls. Results We identified eight circadian rhythm-associated feature genes, among which CHRNA1 emerged as a consistently upregulated hub gene in COPD. An exploratory risk stratification model based on these genes exhibited good discriminatory ability in the discovery cohort (AUC = 0.856, 95% CI: 0.806–0.902). Differential expression of CHRNA1 was validated in an independent cohort and correlated significantly with pro-inflammatory immune infiltration, including increased M1 macrophages and CD8 ⁺ T cells. Single-cell transcriptomics further localized CHRNA1 expression predominantly within B cells in COPD lung tissue. In silico drug screening and ceRNA network analysis predicted potential therapeutics (e.g., amitriptyline, rocuronium bromide) and regulatory miRNAs/lncRNAs. Finally, qRT-PCR confirmed a marked upregulation of CHRNA1 in peripheral blood from COPD patients ( *p*  < 0.0001). Conclusions Our findings suggest that CHRNA1 may serve as a candidate circadian rhythm‑associated immunomodulator in COPD. It demonstrates consistent upregulation across cohorts and shows a significant association with pro‑inflammatory immune infiltration. Single-cell analysis revealed that CHRNA1 is predominantly expressed in pulmonary B cells. The exploratory risk stratification model and predicted therapeutic candidates highlight the translational potential of targeting circadian disruption in COPD, though prospective validation is needed before clinical application.

Serum and hepatic lipid fractions and total protein status of laying hens supplemented with tarragon (Artemisia dracunculus L.) under different stocking densities

Moisture-induced surface degradation mechanism of argyrodite Li6PS5Cl under dry-room conditions

Sustainable conversion of waste plastics to biofuel: Process insights and fuel characteristics

Plastic consumption has become pervasive in modern society, with over 300 million metric tonnes produced annually worldwide, contributing significantly to municipal waste. In Bangladesh, where annual per capita plastic use has risen to 22 kg as of 2022, innovative solutions for managing plastic waste are urgently needed. This research introduces a novel approach to the pyrolysis of various plastics (PET, PVC, PP, HDPE) within a temperature range of 300 °C to 550 °C to produce pyrolytic bio-oil and biochar. We established optimal conditions for each plastic type—500 °C for PET, PVC, and HDPE, and 450 °C for PP—resulting in maximized yields of high-quality liquid oils (61.3% for PP and 47.23% for HDPE). Unique to this study, we innovatively adjust the pyrolysis process parameters to enhance the yield and quality of the derived bio-oils, tailored specifically to the types of plastics treated. The liquid products were characterized as predominantly consisting of C6–C16 hydrocarbons, aligning them closely with naphtha, gasoline, and diesel specifications, suitable for use as renewable fuels. Furthermore, our research applies FTIR and GC-MS analyses in a novel way to provide a detailed examination of these bio-oils, revealing significant quantities of paraffinic hydrocarbons in PP and olefins and naphthenes in HDPE, contributing to their potential fuel applications. The solid char byproducts were also comprehensively characterized using SEM and XRD, providing insights into their suitability for various industrial applications. This study not only demonstrates the potential of pyrolysis to transform waste plastics into valuable renewable energy resources but also advances the technological framework for sustainable waste management practices, marking a significant leap forward in the efficiency and application of plastic waste conversion technologies.

Reconstructing overall competitiveness efficiency using the SCP paradigm with evidence from Saudi Arabian construction contractors

Ribo-ITP enables identification of translons from limited input samples

Reproducibility of fetal global longitudinal strain measured with speckle tracking echocardiography using a fixed cardiac cycle

Fetal speckle tracking echocardiography is an ultrasound-based technique used to assess myocardial velocity and deformation of the fetal heart. Despite its potential, the method has not yet been integrated into routine pregnancy care, partly due to concerns about inconsistent reproducibility. This study aimed to evaluate the intra- and inter-observer reproducibility of global longitudinal strain measurements derived from a fixed fetal cardiac cycle, using speckle tracking echocardiography. Healthy women with singleton pregnancies were enrolled during the second trimester. From enrolment until delivery, four-chamber view clips of the fetal heart were acquired every four weeks. For intra-observer reproducibility, a single observer analyzed the same heart cycle in one DICOM clip twice for global longitudinal strain in the left and right ventricles, with a minimum interval of two weeks between assessments in a blinded manner. For inter-observer reproducibility, two independent observers analyzed the same cardiac cycle within one DICOM clip. A total of 124 women were included, yielding 632 ultrasound clips. Intra-observer reproducibility was poor to moderate for global longitudinal strain for the right and left ventricles. Inter-observer reproducibility demonstrated moderate to good reproducibility for global longitudinal strain in both ventricles. The reproducibility was generally higher in the left ventricle than in the right, and the highest reproducibility was observed before 32 weeks of gestation. In conclusion, speckle tracking echocardiography during pregnancy showed variable reproducibility of strain analysis when performed on a fixed cardiac cycle, with more consistent results in the left ventricle. These findings support the potential utility of fetal speckle tracking echocardiography, while highlighting the need for further refinement of reproducibility before clinical implementation.

Rootstock-dependent scion effects shape root transcriptomes in grafted Pinus pinaster

Abstract Maritime pine ( Pinus pinaster Aiton) is a key conifer in southwestern Europe showing ecological plasticity and economic importance. Understanding molecular interactions between above- and below-ground organs is essential for improving forest resilience and management under changing environmental conditions. Previous transcriptomic analyses of a full-sib family using progenitors from contrasting precipitation regions identified differences between drought-tolerant and drought-sensitive genotypes. Grafting experiments further indicated that rootstocks can modulate transcriptomes in aerial tissues. Here, we analyse truTranscriptomes were influenced by scion origin in a rootstock-dependent manner. In tolerant rootstocks, scions from an arid-region promoted transcriptional patterns related to resource conservation and root remodelling. Scions from a wetter region triggered transcriptional patterns related to broader metabolic adjustments. Sensitive rootstocks showed greater transcriptional responsiveness to scion identity, particularly in pathways related to primary metabolism, osmolyte synthesis and growth. A shared set of genes was consistently regulated across graft combinations, suggesting conserved components of scion–root communication. These findings provide the first comprehensive description of root transcriptomic variation in grafted maritime pine and advance a whole-plant framework for understanding molecular integration in conifers.

East-west divergence and metabolic differentiation in celery domestication

Integrative network pharmacology, transcriptomics, and molecular docking identify candidate Centella asiatica constituents and targets in neurodegenerative diseases

Background Neurodegenerative diseases, including Alzheimer’s disease (AD), Parkinson’s disease (PD), and Huntington’s disease (HD), are progressive disorders with limited therapeutic options. Centella asiatica ( C. asiatica ), a medicinal and edible plant, has been reported to exert neuroprotective and anti-neuroinflammatory properties. Yet, the mechanisms underlying its effects against neurodegenerative diseases remain largely unclear. Methods We employed an integrative strategy combining network pharmacology, transcriptomic analyses, machine learning and molecular docking to prioritize disease-associated molecular networks and candidate compound–target relationships in AD, PD and HD. Results Sixteen candidate constituents of C. asiatica met the predefined drug-likeness, gastrointestinal absorption and blood–brain barrier permeability criteria, yielding 370 unique predicted targets. Disease-gene mining identified 983 AD-associated genes, 1,103 PD-associated genes, and 3,316 HD-associated genes. Integration of compound targets, disease-associated genes, and transcriptomic profiles prioritized five hub genes in PD (CCKAR, MAPK8, PSEN2, SLC6A3, and TH), four in AD (APP, PGK1, PIK3CA, and TTR), and four in HD (CHRND, HSP90AA1, PRKCQ, and TH). Enrichment analyses highlighted disease-relevant processes involving neurotransmitter signalling, cAMP and calcium pathways, MAPK-related responses and inflammatory regulation. ROC analyses provided additional support for the discriminatory performance of the prioritized genes in independent datasets, whereas molecular docking identified favourable predicted Vina docking scores and structurally plausible interactions between selected compounds and hub targets. Conclusion This integrative computational analysis prioritizes candidate C. asiatica constituents, putative disease-associated targets, and molecular pathways in AD, PD, and HD. The findings provide a foundation for subsequent biochemical, cellular, and in vivo validation.

Hygiene practices and associated factors among meat handlers in Halaba Town, Central Ethiopia

Unify learns cellular evolution with universal multimodal embeddings

Abstract Integrating single-cell RNA-sequencing (scRNA-seq) data across species is hindered by evolutionary divergence, technical batch effects, and the reliance on one-to-one orthologs. Here, we present Unify, a transfer learning methodology that learns universal cell embeddings by defining functionally coherent, multi-modal macrogenes. This is achieved by combining RNA expression with embeddings from protein language models and general-purpose language models. Unify transcends species boundaries, enabling cross-species comparisons beyond strict gene-level homology. Unify corrects batch effects while preserving conserved biological signals across vast evolutionary distances and enables more accurate prediction of perturbation responses across species, such as from mouse to human. Applied to species separated by over 700 million years, Unify reconstructs more accurate multi-species cell-type evolutionary trees and uncovers convergent gene programs. Together, these results establish Unify as a powerful method for comparative single-cell genomics and evolutionary biology.

Correction: Gold wrist-assisted PFNA reduces internal complications and enhances recovery in obese osteoporotic patients with intertrochanteric femur fractures

A novel reliability-aware performance assessment for photonic ML analyzers using Frank norm

Manipulatable Cascade Pumping Single-Photon Upconversion

In vitro and in vivo antibacterial activities and phytochemical screening of 80% methanol extract from Ehretia cymosa leaves

Antibiotic resistance has emerged as one of the most urgent global health threats, undermining the effective treatment of bacterial infections. In response, scientific interest is increasingly focused on identifying natural and effective antimicrobial agents derived from medicinal plants. In Ethiopia, Ehretia cymosa ( E. cymosa ) is traditionally used to treat wound infections, fever, gastric ulcers, dysentery, and toothache. However, there is limited scientific evidence to support these traditional claims. Hence, the present study aimed to evaluate the in vitro and in vivo antibacterial activities and to screen the phytochemical profile of the 80% methanol extract of E. cymosa leaves. The air-dried and powdered leaves of E. cymosa were extracted using cold maceration with 80% methanol. The antibacterial activity of the crude extract was tested using the disk diffusion method against selected bacterial pathogens commonly associated with infections. An in vivo model of burn followed by infection was established in mice. Qualitative phytochemical screening was also performed. One-way analysis of variance followed by Tukey’s post hoc multiple tests was used to compare the means of all parameters. The leaves of E. cymosa demonstrated significant antibacterial activity (p < 0.001) against the tested bacterial strains in a dose-dependent manner compared with the control. The minimum inhibitory concentration ranged from 6.25 to 75 mg/mL, while the minimum bactericidal concentration against P. aeruginosa and E. coli was 200 mg/mL. In the in vivo model, the extract resulted in faster wound contraction and a shorter epithelialization period against S. aureus than against P. aeruginosa . The plant leaf is also rich in flavonoids, terpenoids, and tannins. The 80% methanol extract of E. cymosa leaves exhibited antibacterial activity in vitro and in vivo, which corroborates the traditional use of the leaves against infectious diseases. Further studies involving the isolation and characterization of the active compounds are recommended.