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PurposeThe purpose of this paper is to investigate the organizational readiness perspective of adopting artificial intelligence and big data analytics in the textile and garment industry in Bangladesh along with identifying the associated factors.Design/methodology/approachThe research uses a qualitative method using semi-structured interviews with representatives of business organizations and stakeholders of Bangladesh’s textile and garment industry.FindingsThe research reveals that the textile and garment industry in Bangladesh currently has low organizational readiness to adopt artificial intelligence and big data analytics. This is due to moderate knowledge- and leadership-readiness along with low human-, finance- and engagement-readiness of most of the business organizations. The readiness aspects interplay with each other and need to be improved holistically.Practical implicationsConsidering the significant global and national importance of Bangladesh’s textile and garment industry, gaining insights into the industry’s current state of readiness for adopting artificial intelligence and big data analytics would offer valuable assistance to both national and global economies and may enhance economic outcomes.Originality/valueSince no exploratory study was conducted to understand the organizational readiness aspects of adopting artificial intelligence and big data analytics of the globally significant textile and garment industry in Bangladesh, the paper analyzes five key aspects of such readiness and offers a basis for conducting similar studies in other emerging economies.

Alopecia areata (AA) and Androgenic alopecia (AGA) are the most common multifactorial hair loss disorders that have a serious psychological impact on the affected individuals, while frontal fibrosing alopecia (FFA) is comparatively less common. However, due to the unknown etiology and the effect of many adverse factors, the prognosis of these conditions is challenging to predict. Moreover, no approved therapy has been available to date to prevent or treat these disorders. MicroRNAs (miRNAs) are a group of evolutionary conserved small non-coding RNA molecules with significant roles in the posttranscriptional gene regulation either through mRNA degradation or translational repression. A number of biological processes are controlled by these molecules, including cell growth and differentiation, proliferation, inflammation, immune responses, and apoptosis. Recently, a handful of studies have demonstrated the impact of miRNAs on common hair loss-related disorders; however, the exhaustive molecular mechanisms are still unclear. In this review, we discussed the functional implications of miRNAs in common hair loss-related disorders and addressed their efficacy to be used for theranostic purposes shortly.

Aberrant pro-survival signaling is a hallmark of cancer cells, but the response to chemotherapy is poorly understood. In this study, we investigate the initial signaling response to standard induction chemotherapy in a cohort of 32 acute myeloid leukemia (AML) patients, using 36-dimensional mass cytometry. Through supervised and unsupervised machine learning approaches, we find that reduction of extracellular-signal-regulated kinase (ERK) 1/2 and p38 mitogen-activated protein kinase (MAPK) phosphorylation in the myeloid cell compartment 24 h post-chemotherapy is a significant predictor of patient 5-year overall survival in this cohort. Validation by RNA sequencing shows induction of MAPK target gene expression in patients with high phospho-ERK1/2 24 h post-chemotherapy, while proteomics confirm an increase of the p38 prime target MAPK activated protein kinase 2 (MAPKAPK2). In this study, we demonstrate that mass cytometry can be a valuable tool for early response evaluation in AML and elucidate the potential of functional signaling analyses in precision oncology diagnostics. The molecular mechanisms underlying response to chemotherapy in Acute myeloid leukemia (AML) remain to be explored. Here, the authors perform 36-dimensional mass cytometry in 32 AML patients during intensive chemotherapy and suggest functional signalling analysis for prognosis prediction early after treatment in AML.

The bone regenerative potential of bone marrow‐derived mesenchymal stromal/stem cells (MSC) is critically shaped by their interaction with the host immune system. Here, we compare two MSC priming strategies: coculture with regulatory T cell (Treg) or exposure to the proresolving mediator Resolvin E1 (RvE1) to enhance ectopic osteogenesis in mice. Both approaches enhance MSC‐mediated bone formation through distinct local and systemic immunomodulatory mechanisms. Treg priming promotes a regulated anti‐inflammatory environment characterized by reduced IL‐6 protein levels (3.6‐fold), sustained IL‐13 elevation up to 10 weeks (1.5‐fold compared with RvE1), early suppression of cytotoxic CD8+ T cells and IFN‐γ production by CD4+ T cells in lymph nodes, a >25% reduction in fibrous capsule formation, and enhanced B cell activity, collectively supporting ossification. In contrast, RvE1 priming promotes early TNF‐α and late granulocyte‐macrophage colony‐stimulating factor (GM‐CSF) production (1.2‐fold compared with Treg) within the scaffold, facilitating osteoblast maturation. Transcriptomic profiling revealed distinct osteogenic signatures, with amphiregulin uniquely upregulated in the Treg group (>7‐fold). Using a Treg depletion model, we further demonstrate that RvE1 retains partial immunomodulatory capacity and supports matrix organization. Together, these findings underscore the versatility of immune modulation in directing MSC fate and highlight the potential of tailoring immunoregulatory strategies to optimize bone regeneration. Bone regeneration by mesenchymal stem cells is strongly influenced by immune signals. This study shows that priming stem cells with regulatory immune cells or an inflammation‐resolving lipid molecule enhances bone formation through distinct immune pathways. The findings highlight how tailored immune modulation can direct stem cell fate and improve regenerative outcomes.

Cervical cancer (CC) is a major global health problem with 570,000 new cases and 266,000 deaths annually. Prognosis is poor for advanced stage disease, and few effective treatments exist. Preoperative diagnostic imaging is common in high-income countries and MRI measured tumor size routinely guides treatment allocation of cervical cancer patients. Recently, the role of MRI radiomics has been recognized. However, its potential to independently predict survival and treatment response requires further clarification. This retrospective cohort study demonstrates how non-invasive, preoperative, MRI radiomic profiling may improve prognostication and tailoring of treatments and follow-ups for cervical cancer patients. By unsupervised clustering based on 293 radiomic features from 132 patients, we identify three distinct clusters comprising patients with significantly different risk profiles, also when adjusting for FIGO stage and age. By linking their radiomic profiles to genomic alterations, we identify putative treatment targets for the different patient clusters (e.g., immunotherapy, CDK4/6 and YAP-TEAD inhibitors and p53 pathway targeting treatments).

MicroRNAs (miRNAs) are a class of endogenous, small (20–24 nucleotides), non-coding RNA molecules that regulate gene expression through the miRNA-mediated mechanisms of either translational inhibition or messenger RNA (mRNA) cleavage. In the past years, studies have mainly focused on elucidating the roles of miRNAs in vascular plants as compared to non-vascular plants. However, non-vascular plant miRNAs have been predicted to be involved in a wide variety of specific biological mechanisms; nevertheless, some of them have been demonstrated explicitly, thus showing that the research field of this plant group owns a noteworthy potential to develop novel investigations oriented towards the functional characterization of these miRNAs. Furthermore, the insights into the roles of miRNAs in non-vascular plants might be of great importance for designing the miRNA-based genetically modified plants for valuable secondary metabolites, active compounds, and biofuels in the future. Therefore, in this current review, we provide an overview of the potential roles of miRNAs in different groups of non-vascular plants such as algae and bryophytes.

A large proportion of the global tuberculosis (TB) burden is asymptomatic and not detectable by symptom-based screening, driving the TB epidemic through continued M. tuberculosis transmission. Currently, no validated tools exist to diagnose incipient and subclinical TB. Nested within a large prospective study in household contacts of pulmonary TB cases in Southern India, we assessed 35 incipient TB and 12 subclinical TB cases, along with corresponding household active TB cases (n=11), and household controls (n=39) using high throughput methods for transcriptional and protein profiling. We split the data into training and test sets and applied a support vector machine classifier followed by a Lasso regression model to identify signatures. The Lasso regression model identified an 11-gene signature ( ABLIM2, C20orf197, CTC-543D15.3, CTD-2503O16.3, HLADRB3, METRNL, RAB11B-AS1, RP4-614C10.2, RNA5SP345, RSU1P1 , and UACA ) that distinguished subclinical TB from incipient TB with a very good discriminatory power by AUCs in both training and test sets. Further, we identified an 8-protein signature comprising b-FGF, IFNγ, IL1RA, IL7, IL12p70, IL13, PDGF-BB, and VEGF that differentiated subclinical TB from incipient TB with good and moderate discriminatory power by AUCs in the training and test sets, respectively. The identified 11-gene signature discriminated well between the distinct stages of the TB disease spectrum, with very good discriminatory power, suggesting it could be useful for predicting TB progression in household contacts. However, the high discriminatory power could partly be due to over-fitting, and validation in other studies is warranted to confirm the potential of the immune biosignatures for identifying subclinical TB.

Background Next-generation sequencing (NGS) is well established in clinical diagnostics, and whole-genome sequencing (WGS) is increasingly becoming the method of choice, as a result of lower prices and robust comprehensive data. While guidelines exist for variant interpretation and laboratory quality considerations, there remains a need for standardised bioinformatics practices to ensure clinical consensus, accuracy, reproducibility and comparability. Methods This article presents consensus recommendations developed by 13 clinical bioinformatics units participating in the Nordic Alliance for Clinical Genomics (NACG) by expert bioinformaticians working in clinical production. The recommendations are based on clinical practice and focus on analysis types, test and validation, standardisation and accreditation, as well as core competencies and technical management required for clinical bioinformatics operations. Results Key recommendations include adopting the hg38 genome build as reference, and a standard set of recommended analyses, including the use of multiple tools for structural variant (SV) calling and in-house data sets for filtering recurrent calls. Clinical bioinformatics in production should operate at standards similar to ISO 15189, utilising off-grid clinical-grade high-performance computing systems, standardised file formats and strict version control. Reproducibility should be ensured through containerised software environments. Pipelines must be documented and tested for accuracy and reproducibility, minimally covering unit, integration and end-to-end testing. Standard truth sets such as GIAB and SEQC2 for germline and somatic variant calling, respectively, should be supplemented by recall testing of real human samples that have been previously tested using a validated method. Data integrity must be verified using file hashing, while sample identity must be confirmed through fingerprinting and genetically inferred identification markers such as sex and relatedness. Finally, clinical bioinformatics should encompass diverse skills, including software development, data management, quality assurance and domain expertise in human genetics. Conclusions These recommendations provide a consensus framework for standardising bioinformatics practices across clinical WGS applications and can serve as a practical guide to facilities that are new to large-scale sequencing-based diagnostics, or as a reference for those who already run high-volume clinical production using NGS.

MicroRNAs (miRNAs) are small noncoding RNA molecules that play crucial post-transcriptional regulatory roles in plants, including development and stress-response signaling. However, information about their involvement in secondary metabolism is still limited. Murraya koenigii is a popular medicinal plant, better known as curry leaves, that possesses pharmaceutically active secondary metabolites. The present study utilized high-throughput sequencing technology to investigate the miRNA profile of M. koenigii and their association with secondary metabolite biosynthesis. A total of 343,505 unique reads with lengths ranging from 16 to 40 nt were obtained from the sequencing data, among which 142 miRNAs were identified as conserved and 7 as novel miRNAs. Moreover, 6078 corresponding potential target genes of M. koenigii miRNAs were recognized in this study. Interestingly, several conserved and novel miRNAs of M. koenigii were found to target key enzymes of the terpenoid backbone and the flavonoid biosynthesis pathways. Furthermore, to validate the sequencing results, the relative expression of eight randomly selected miRNAs was determined by qPCR. To the best of our knowledge, this is the first report of the M. koenigii miRNA profile that may provide useful information for further elucidation of the involvement of miRNAs in secondary metabolism. These findings might be crucial in the future to generate artificial-miRNA-based, genetically engineered M. koenigii plants for the overproduction of medicinally highly valuable secondary metabolites.