From the laboratory perspective, effective management of patients with chronic myeloid leukemia (CML) requires accurate diagnosis, assessment of prognostic markers, sequential assessment of levels of residual disease and investigation of possible reasons for resistance, relapse or progression. Our scientific and clinical knowledge underpinning these requirements continues to evolve, as do laboratory methods and technologies. The European LeukemiaNet convened an expert panel to critically consider the current status of genetic laboratory approaches to help diagnose and manage CML patients. Our recommendations focus on current best practice and highlight the strengths and pitfalls of commonly used laboratory tests. BCR) gene at 22q11 and the Abelson Proto-oncogene 1 Nonreceptor Tyrosine Kinase (ABL1) gene at 9q34 [BCR::ABL1 fusion, located on the Ph chromosome, encodes a chimeric BCR::ABL1 protein with deregulated tyrosine kinase activity that is the primary driver of the pathogenesis of CML [Chronic myeloid leukemia (CML) is characterized by the Philadelphia (Ph) chromosome, described in 1960 as the first recurrent chromosome abnormality associated with a human malignancy , which ls of CML . Followis of CML , 8. In mBCR::ABL1 tyrosine kinase domain (TKD) that result in impairment of drug binding. In some cases, poor responses are associated with progression from CP towards an aggressive and usually terminal acute leukemia, known as blast crisis or blastic phase (BP), which may be of myeloid, lymphoid or mixed phenotype [Treatment goals for CML have advanced well beyond considerations of survival to molecular assessments of the depth and stability of remission, and the possibility of attaining treatment-free remission (TFR) . Despitehenotype .All tests for which the results are used for clinical management should be conducted in appropriately accredited laboratories, e.g., to ISO15189.2022, and fully validated before clinical use.Testing laboratories should participate in appropriate external quality assurance (EQA) schemes.BCR::ABL1 positive disease only. Diagnosis of CML therefore requires the detection of the t and/or BCR::ABL1 in the appropriate clinical and laboratory setting. Cases previously considered as BCR::ABL1-negative CML should be classified as another myeloid neoplasm depending on their hematological and molecular features [Since 2008, CML has been defined as ;22)q34;q and/or Bfeatures .BCR and ABL1 genes in interphase cells, and/or reverse transcription polymerase chain reaction (RT-PCR) to detect BCR::ABL1 mRNA as first line diagnostic tools in patients with a clinical suspicion of CML, with cytogenetic follow up for confirmation and detection of ACAs. Used in isolation, all three tests have limitations that need to be considered.Historically, cytogenetic analysis of peripheral blood or bone marrow-derived cells played an essential role in the initial diagnosis of CML by direct detection of the t, but many centres nowadays routinely use fluorescence in situ hybridisation (FISH) to detect juxtaposition of the BCR::ABL1. A bone marrow aspirate is preferred for cytogenetic analysis and can be performed on the same sample taken for morphological analysis to diagnose disease phase, but a karyotype may be successfully obtained from peripheral blood in many cases at diagnosis (but not at follow up when bone marrow is required). In 5-10% of cases the karyotype shows a variant translocation that typically involves either or both 9q34 and 22q11 plus one or more additional chromosomes. The remaining 1-5% of cases have a cryptic BCR::ABL1 fusion without any cytogenetically-visible involvement of chromosomes 9 or 22. These cases typically arise by small double recombination events that insert ABL1 into BCR [At presentation, up to 85–90% of CML cases have a t on cytogenetic analysis and thus have a standard Ph chromosome, sometimes described as 22q- or der22. A minimum of 20 metaphases should be analysed  to ensure detection of ACAs. In most patients ≥95% of cells are found to be Ph-positive, which reflects the marked expansion of clonal myeloid progenitor cells induced by into BCR , 14 and BCR::ABL1 rearrangements are not thought to be of any prognostic significance [Around 7% of CP cases present with ACAs in the CML clone that are unrelated to the t9;22). ACAs are also acquired with higher frequency during the course of the disease and are much more common in BP . Clonal ificance , 20, 21.. ACAs arificance –25. Cytoificance  and use ificance .BCR::ABL1 rearrangement irrespective of any cytogenetic or molecular nuances, but it does not detect ACAs. Interphase FISH is widely employed as an initial screening tool for CML, with the analysis usually being performed on peripheral blood leukocytes. Interphase FISH on blood granulocytes may also be useful to differentiate between de novo lymphoid BP-CML (granulocytes positive for BCR::ABL1) and de novo BCR::ABL1-positive acute lymphoblastic leukemia . Metaphase FISH may be useful to confirm the presence and location of BCR::ABL1 in cases with variant translocations or cryptic rearrangements. FISH negative, BCR::ABL1 positive patients may theoretically arise due to very small double recombination events but these are exceedingly rare and, to our knowledge, only a single fully validated FISH negative CML case has been described [FISH using commercially available probes efficiently detects essentially all types of escribed .ABL1 locus, but splicing of the primary transcript gives rise to a limited number of BCR::ABL1 mRNA isoforms [BCR exon 13 spliced to ABL1 exon 2) and e14a2 (BCR exon 14 spliced to ABL1 exon 2). Historically, these two chimeric mRNAs were referred to as b2a2 and b3a2, with b2 (BCR exon 13) and b3 (BCR exon 14) corresponding to the second and third exons within the classically-defined major breakpoint cluster region (M-BCR) [BCR exon 14. Collectively, e13a2 and e14a2 BCR::ABL1 account for 98% of CML cases, with the majority of these expressing e14a2. Around 10% of cases express both isoforms. A large international survey of CML cases found small but significant differences in the relative prevalence of the two fusions in relation to age and gender, with the prevalence of e13a2 being higher in males compared to females and lower in the elderly [BCR::ABL1 have inferior molecular responses compared to those expressing e14a2 [The t genomic breakpoints are widely dispersed, particularly at the isoforms . The two (M-BCR) . Both e1 elderly . In adding e14a2 –34, an oBCR::ABL1 fusions that arise from BCR breakpoints outside the M-BCR or downstream of ABL1 exon 2. The most common are e1a2, e6a2, e8a2, e19a2, e13a3, and e14a3, i.e. mRNA fusions of BCR exons 1, 6, 8, 19 to ABL1 exon 2, or BCR exons 13 or 14 fused to ABL1 exon 3  and 230 kDa (p230) BCR::ABL1, respectively.Around 2% of CML patients express atypical ABL1::BCR, is expressed in approximately 70% of CML patients but is not itself believed to play a role in the disease process [ABL1::BCR expression is linked to deletions at the genomic breakpoints on the der9,t [The reciprocal product of the t,  process , 36. Howq34;q11) . Largelyq34;q11) , 39.BCR::ABL1 in cases with suspected CML, it is essential that the methodology detects both typical and atypical BCR::ABL1 variants. Screening only for transcripts that encode p210 or p210 plus p190 BCR::ABL1 is poor practice and may result in up to 2% of bone fide CML cases being misdiagnosed as another disorder. If a limited transcript screen is performed it is essential that the clinical report clearly states that atypical BCR::ABL1 fusions would not have been detected, and that a diagnosis of CML is not excluded with certainty.For centres that use reverse transcription polymerase chain reaction (RT-PCR) based methods as the primary or sole means to screen for BCR::ABL1 transcript type in positive cases should be determined prior to starting treatment to facilitate accurate assessment of measurable residual disease (MRD) on therapy. Although the exact transcript type should ideally be identified for all patients, many groups use reverse transcriptase quantitative PCR (RT-qPCR) to assess levels of e13a2 and/or e14a2 mRNA in cases prior to treatment and only search for atypical fusions in the small minority of CML cases for whom BCR::ABL1 mRNA is not detected, or found at unexpectedly low levels . Low levels of BCR::ABL1 mRNA at diagnosis may also signal the presence of a co-existing BCR::ABL1-negative hematological neoplasm. Quantitative assessment of BCR::ABL1 mRNA prior to treatment may also be useful to help interpret initial MRD results, e.g. after 3 months on therapy [For centres that use cytogenetics and/or FISH as the primary screen, the  therapy –42, as dBCR::ABL1 variants may be challenging, and there are no CE-marked or Food and Drug Administration (FDA)-approved kits or systems that can detect most rare isoforms. Laboratory-developed tests (LDTs) have been described that use multiplex RT-PCR [BCR::ABL1 isoforms as well as other fusion genes.Identification of atypical x RT-PCR  followedBCR::ABL1 fusions with an unusually aggressive or benign clinical course [BCR::ABL1, the predominant isoform in BCR::ABL1-positive ALL but also seen in 1% of CML cases. This isoform was originally associated with a phenotype intermediate between CML and chronic myelomonocytic leukemia (CMML) [BCR::ABL1 transcript type. Nevertheless, identification of cases who express an atypical BCR::ABL1 variant is required prior to treatment to enable appropriate follow up analysis.Several case reports have associated individual atypical l course , but thea (CMML) , and mora (CMML) –46. Othea (CMML)  and consBCR::ABL1 isoform in any given patient is stable over time since it is determined by the position of the t genomic breakpoints. However, the sensitivity and specificity of RT-qPCR may occasionally cause some confusion. For example, rearrangements that produce e13a2 and/or e14a2 BCR::ABL1 have the capability to generate e1a2 mRNA and other isoforms by alternative splicing  some studies have found that detection of ASXL1 mutations in CP is associated with inferior response to TKI treatment.Once a diagnosis of CML has been made, additional laboratory investigations may help to define prognosis Table . MultiplBCR::ABL1 TKD mutations) have been described in 20-30% of CML patients in CP, but it is important to note that most published studies are retrospective and focused on patients who subsequently relapsed or progressed to BP. Prospective studies are needed to determine the true frequency of genetic variants in CP, and their clinical significance. Nevertheless, ASXL1 has emerged as the most common somatically-mutated gene in CP [RUNX1 (2%), TET2 (2%), and DNMT3A (2%), and mutations or deletions of IKZF1 (4%). Of note, DNMT3A, TET2 and ASXL1 mutations are commonly associated with CH and in some cases may precede the acquisition of BCR::ABL1 [ASXL1 mutations [Overall, variants in cancer-associated genes  loci have all been associated with response to TKI therapy or outcome after treatment cessation [As for constitutional genetics, a specific essation –63 but iASXL1, RUNX1, BCOR/BCORL1 and IKZF1 each being seen in 15-20% of cases [ASXL1 and TP53 mutations are usually associated with myeloid BP, whereas IKZF1 and CDKN2A/B mutations/deletions are associated with lymphoid BP [SETD1B and UBE2A may be relatively specific to CML-BP. Although mutational patterns have been linked to prognosis, potentially targetable abnormalities are uncommon, e.g., IDH1/2 and NRAS mutations in 5% of cases and FLT3 mutations in very rare cases [CBFB::MYH11 and rearrangements that target the MECOM locus. More recently, whole transcriptome sequencing (WTS) or targeted RNA sequencing has revealed that acquired fusion genes are more common than previously thought, with some targeting well known cancer genes such as RUNX1, but also others of uncertain pathogenicity [The great majority of patients who present with, or progress to, BP have additional somatic mutations in cancer-associated genes, with of cases , 52, 64.phoid BP , 66. Mutgenicity , 67.From a technical perspective, there are several points to consider in relation to somatic mutation screening. For example, the methodology , the limit of detection , the sample type  and the value of including germline samples in the analysis. Currently, most routine NGS analysis for hematological neoplasms is undertaken using targeted panels with an LoD of 3–5% variant allele frequency , a level corresponding to the technical sensitivity rather than a clinically-validated cut off. Error-corrected NGS can achieve better sensitivity (LoD ≤0.5%) but in the context of CML there is no evidence that the detection of low-level mutations is of any clinical benefit. Analysis of germline samples is typically limited to investigation of specific germline predisposition syndromes.RUNX1, ASXL1, BCOR, BCORL1, TP53, IZKF1 [ASXL1 c.1934dup;p.Gly646Trpfs*12 (ASXL1 c.1934dup) variant is widely seen in myeloid neoplasms, including CML, but it also occurs as a common technical artefact by PCR replicative slippage [ASXL1 c.1934dup are highly dependent on the methodology used but may exceed 10%. It is essential that any laboratory reporting this variant establishes the background call rate with appropriate control samples (which is complicated by the fact that ASXL1 variants are associated with CH) and only reports variants that are considered to have a high probability of being real, as described [https://varnomen.hgvs.org/) and include an assessment of relevance and pathogenicity as well as the variant allele frequency. Accurately ascribing pathogenicity to somatic variants  is a complex process but structured classification guidelines continue to improve and should be employed [BCR::ABL1 levels since somatic variants could be derived from non-leukemia cells and represent CH or occasionally another disorder. Finally, the possibility of a likely irrelevant germline variant should be considered and potentially excluded when the VAF is close to 50% or 100%.An ideal CML panel should cover both myeloid and lymphoid targets , for exa3, IZKF1 , and the3, IZKF1 . It is islippage . The VAFescribed . Detaileescribed , 73. Repemployed –76. CareRNA studies have revealed additional potentially important markers that may be incorporated into future diagnostic algorithms such as gene expression signatures predictive of resistance and/or progression , 78 and ASXL1 mutation screening, is not currently recommended for routine clinical management but should be performed in investigational studies.Gene panel analysis pretreatment, including BCR::ABL1.NGS panel analysis for patients who present in, or progress to, BP is recommended to identify potential targets for treatment in addition to BCR::ABL1 mRNA in peripheral blood leukocytes from patients on treatment serve as a surrogate for disease burden, and strongly correlate with outcome. Reverse transcription digital PCR (RT-dPCR) is a valid alternative to RT-qPCR and, when optimised, can provide more accurate results across a broad spectrum of disease levels but may be of particular value at very low levels of disease [BCR::ABL1 variants. Similarly, the use of qualitative RT-PCR analysis is not recommended but may be of some value in resource-limited settings to rule out relapse or non-adherence [Sequential molecular monitoring of CML patients by reverse transcription quantitative polymerase chain reaction (RT-qPCR) has been established for many years using either LDTs or commercial kits/systems to assess the depth of clinical response to TKIs or identify early relapse after stem cell transplantation . Levels  disease . Follow dherence . FinallyBCR::ABL1, the number of ABL1, GUSB or BCR reference gene transcripts is determined for all samples to gauge their quality, and to estimate the upper level of MRD in samples for which BCR::ABL1 mRNA is not detected [ABL1 is used in the great majority of testing laboratories. Other reference genes are not recommended because they have not been calibrated to the International Scale (IS) for BCR::ABL1 measurement [BCR::ABL1/reference gene expression levels should be reported on the IS, whether derived by RT-qPCR or RT-dPCR. The IS defines MRD levels as a percentage relative to the standardized baseline used in the IRIS trial , the number of reference gene transcripts in the same volume of cDNA used to test for BCR::ABL1 indicates the sensitivity for that particular sample  is broadly equivalent to complete cytogenetic remission [4 or deeper, irrespective of whether BCR::ABL1 is detected or not. Terms such as ‘complete molecular response’ or 0% BCR::ABL1IS should not be used because the interpretation of negative results is only possible if the sensitivity for the sample in question is known, as indicated by the number of reference gene transcripts . Failure to detect BCR::ABL1 for a patient with MRD may simply be related to a technical issue such as degraded RNA. For example, undetectable BCR::ABL1 mRNA with 45,000 ABL1 reference gene transcripts indicates MR4.5, but undetectable BCR::ABL1 mRNA with <10,000 ABL1 reference gene transcripts is not evaluable for DMR and should be considered as a technical failure  laboratory-specific conversion factors (CFs) to the IS derived by sample exchange with an established reference laboratory , 87, 88,CR::ABL1 , 89 or  peripheral blood samples should be of adequate volume  and delivered to the testing laboratory as soon as possible, ideally within 24 hours and no later than 48 hours. RNA should be extracted from total leukocytes, not mononuclear cells, or any other cell fraction. In resource-poor settings with no local facilities to perform MRD analysis, dried blood spots may be sent by regular mail at ambient temperature to a suitable reference laboratory, although the test sensitivity is not as good as that performed on fresh samples .BCR::ABL1 and the chosen reference gene are amplified with comparable efficiencies, a parameter that is influenced in part by the amount of input material [BCR::ABL1 and reference gene targets, e.g. ERM-AD623 [Ongoing performance evaluation  is required to detect assay drift and variability, e.g. by frequent analysis of standards designed to represent high and low levels of disease, ideally on every run in conjunction with the results feeding into run acceptability criteria , 98, 99.material . DiffereRM-AD623 , 100.As an endpoint measurement, RT-dPCR is less susceptible to differences in amplification efficiency compared to RT-qPCR, although the very wide dynamic range required for MRD assessment is a challenge for any technology. Although RT-dPCR is not intrinsically more sensitive than RT-qPCR , 101, thIS and 0.1% BCR::ABL1IS. In an international survey of laboratory performance, the mean standard deviation in BCR::ABL1 measurement was 0.2 log, or approximately 1.6 fold on a linear scale, with a greater degree of variation at lower levels of disease [IS  this degree of variation equates to 0.063–0.16% which may be considered as the UoM at this level. Other recommendations for reporting MRD results and further details on test variability at high and low levels have been described in detail elsewhere [Tests should be fully validated for clinical reporting, e.g., as described . Laborat disease . For exalsewhere , 107.IS at 3 months on treatment), the rate of reduction of BCR::ABL1 mRNA from baseline levels can help to distinguish patients who are destined to do relatively well with no change in treatment from those who may require an alternative TKI [Analysis of the relative clone size pretreatment by RT-qPCR has revealed significant variation between patients. Furthermore, the initial velocity of disease reduction on therapy, usually measured as the halving time, has prognostic value for both early and late response, including TFR –42. Focutive TKI –42.ABL1 actually amplify ABL1 plus BCR::ABL1 and thus the maximum value of BCR::ABL1/ABL1 reaches a plateau. Similarly, the expression levels of both unrearranged BCR alleles are measured when the CML clone is small relative to normal cells, but this reduces to the single unrearranged allele when the clone size is high. The net result of the use of different reference genes is that the IS begins to break down above 10% BCR::ABL1IS. Only GUSB is completely independent of BCR::ABL1, however a recent international comparison revealed relatively high and currently unexplained variability of MRD results between laboratories using GUSB [ABL1 and BCR could in principle be easily corrected mathematically, this does not work well in practice due to differences in amplification efficiencies for BCR::ABL1 and reference genes between laboratories [BCR::ABL1 mRNA from baseline levels is very challenging and specific, generally applicable recommendations cannot be made. Consequently, this remains a research tool. In general, however, the use of GUSB or BCR as reference genes is expected to be of greater value than ABL1 for assessment of early response kinetics although this remains to be proven in prospective studies.Standardized measurement of pretreatment disease levels by RT-qPCR (or RT-dPCR), however, is problematic due to the different properties of the 3 reference genes that have become established for CML MRD analysis. These differences are not relevant for standard MRD assessments, but they result in reference gene-specific distortions at high disease burdens. Most primer/probe sets for ing GUSB . Althougratories . At the BCR::ABL1 variants as the predominant isoform prior to treatment is important to enable appropriate follow up MRD analysis. Atypical fusions are either not detected by standard RT-qPCR or RT-dPCR configurations, or they are detected very inefficiently. Thus, patients may be incorrectly assessed as very good responders to treatment if the presence of an atypical variant is not recognised. MRD analysis for patients with atypical BCR::ABL1 variants needs to be performed with bespoke, validated RT-qPCR or RT-dPCR tests; results cannot be expressed on the IS and thus standard time-dependent molecular milestones and guidelines for treatment discontinuation cannot be applied. However, the general tempo and depth of response can be assessed in these cases as individual molecular response (IMR) levels in relation to baseline (pre-treatment) BCR::ABL1 expression prior to therapy [GUSB as a reference gene to help ensure the linearity of measurements at high disease levels.Identification of cases who express atypical  therapy . Since tBCR::ABL1, there is a growing recognition that cases expressing atypical BCR::ABL1 variants may also be candidates to stop treatment. From a technical perspective, we suggest that persistent molecularly undetectable disease for >2 years and ≥4 log reduction from baseline levels might be used to consider eligibility to stop treatment alongside other clinical requirements. The test employed must have been validated to detect low disease burdens, and evaluable patient samples must have ≥10,000 ABL1 and/or ≥24,000 GUSB transcripts, and ideally ≥32,000 ABL1 and/or ≥77,000 GUSB transcripts .For assessment of deep molecular response, tests should be optimised to achieve a limit of detection of MROngoing internal quality control is essential to monitor assay stability over time.BCR::ABL1 fusions should be monitored by bespoke RT-qPCR or RT-dPCR assays and results expressed as individual molecular responses compared to baseline levels.Patients expressing atypical FISH is not recommended as this technique has very limited sensitivity compared to molecular monitoring but it may be useful if molecular tests for atypical fusions are not available.BCR::ABL1 encoding the TKD are the only ones that are clinically actionable. Mutations may alter critical contact residues between BCR::ABL1 and the inhibitor, or induce conformational changes that result in reduced TKI binding affinity. Rational development of 2G, 3G and 4G TKIs has offered alternative therapeutic options for patients with unsatisfactory responses, including those positive for BCR::ABL1 mutations [50  and in vivo observation of mutations selected in patients with non-responsive disease on a given TKI has helped to understand which imatinib-resistant mutations (and which newly described mutations) may confer resistance to dasatinib, nilotinib, bosutinib, ponatinib , i.e. two mutations in cis on the same BCR::ABL1 molecule), that may be observed in a proportion of patients, mainly advanced phase patients who have failed ≥2 lines of TKI therapy [Among the variety of resistance mechanisms that may underlie a non-optimal response to TKI therapy in CML patients, secondary point mutations in within the region of utations . Over th therapy –121. BasBCR::ABL1 TKD mutations testing at relevant timepoints on treatment , for which the Matched Annotation from the NCBI and the EMBL-EBI (MANE) select versions [By convention, the numbering for BCR::ABL1 resistance-associated mutations is made by reference to the ABL1 isoform a . Multiple mutations between amino acids 217 and 514 have been identified in imatinib-resistant patients, with some falling in key functional regions  detected more frequently than others. Mutants acknowledged to be resistant to 2G TKIs are much fewer . The use of targeted NGS has been explored in several retrospective and prospective studies. NGS presents both advantages and disadvantages. On one hand, its lower LoD  allows more accurate assessment of mutation status, including early detection of emerging mutations that have not yet reached the threshold for Sanger sequencing detection. Moreover, NGS theoretically enables clonal analysis, hence straightforward identification of compound mutations. However, two caveats exist in this context: the spectrum of mutation pairs for which the in cis versus in trans configuration can be assessed is dependent upon a) the sequencing chemistry and read length, with longer reads preferred, and b) the phenomenon of PCR-mediated recombination may impair reliable detection of low-level compound mutations [BCR::ABL1 fusion and not the normal unrearranged ABL1 allele  no commercial kits are available, which poses the need to establish and internally validate an LDT; ii) the throughput of the instruments mandates batching samples in each sequencing run. The need to balance cost-effectiveness and turnaround time makes NGS analysis practically feasible only if samples are centralized in reference laboratories. Implementation of NGS-based mutation detection is particularly challenging in developing countries, where new accessible technologies are required.Widespread implementation of NGS-based approaches of BCR::ABL1 mutation might be considered as a measure of the degree of genetic instability, thus positivity for any mutation might identify higher-risk patients requiring more careful monitoring. In this context, given the wide number of mutations associated with imatinib resistance, a targeted dPCR screen may be of limited value in the setting of a warning response to imatinib, where positivity for an imatinib-resistant mutation would be a decisive indication for TKI switch.Alternative methods have been described that offer greater sensitivity as compared to Sanger sequencing. However, they rely on the use of allele-specific primers or probes, hence they can be implemented only for a limited number of mutations of interest. A multiplexed approach of primer extension coupled with mass spectrometry-based identification of the extended nucleotides (Sequenom MassArray) has been described that allows scanning for 31 different mutations (imatinib- and 2G TKI-resistant) , 143. ThBCR::ABL1 TKD mutation testing is cDNA rather than genomic DNA. The former enables (pre-)amplification and specific analysis of BCR::ABL1 fusion transcripts. In contrast by using DNA both ABL1 and BCR::ABL1 alleles would be sequenced—which would usually result in an inferior LoD because the number of BCR::ABL1 alleles (present only in leukemic cells) are far fewer than the number of ABL1 alleles . Use of high sensitivity techniques like error-corrected NGS or digital PCR might theoretically enable this issue to be circumvented [Set against these considerations is the fact that the clinical utility of TKD mutation detection by Sanger sequencing has been firmly established in multiple retrospective and prospective studies. By contrast, not all low-level mutations identified by NGS necessarily herald relapse. Whatever the method used to detect the nucleotide substitutions, it is generally acknowledged that the best input nucleic acid for umvented , and indumvented , but a sBCR::ABL1 TKD screening [Sequencing chemistries and technologies are continuously evolving, and third generation sequencers that enable real time long  sequencing reads are beginning to enter routine practice. The use of long read Nanopore and PacBio sequencing has been reported for creening , 147, buBCR::ABL1 TKD mutation testing, provided that a reliable and validated assay has been implemented.Targeted NGS, whenever available or accessible, is the recommended method for BCR::ABL1 mRNA levels ≥0.1% IS, i.e., ELN-defined failure or warning.Mutation screening should only be performed for cases with The recommended input material is RNA from peripheral blood or bone marrow leukocytes. Analysis of genomic DNA is not recommended for routine analysis.Digital PCR may be considered as a first-level screening test for detection of specific mutations of interest when (rapid) TKI switch is needed.The use of Sanger sequencing is acceptable whenever NGS is not available or accessible, and does not represent an inappropriate management of patients.The minimal recommended region to be covered in a broad, untargeted mutation screen for 1st, 2nd and 3rd generation TKIs should focus on the sequence encoding the TKD . The frequency of TKI-resistant mutations by NGS in failure and warning cases has been reported to be 40-50% and 30-40%, respectively [It is generally acknowledged that mutations surface as a result of the selective pressure imposed by TKI therapy. At diagnosis, before the start of any TKI treatment, mutations—if any—would be undetectable with the currently available methods. This is likely to hold true both for CP and for the very rare cases who are diagnosed in BP, although data for the latter are extremely scarce . On trea failure , and thuectively .BCR::ABL1 TKD mutation testing is indicated:- In case of failure and warning to 1st or 2nd line TKI therapy according to current ELN clinical recommendations.IS ≥ 0.1%) after allogeneic stem cell transplant if mutation was detectable prior to transplant.- In case of relapse .IS < 0.1%.- In patients with BCR::ABL1BCR::ABL1 TKD mutation testing should be considered when there is no improvement of response after 3–6 months of treatment, and is indicated if response worsens (e.g. ≥5-fold increase of BCR::ABL1ISwith loss of MR3).In patients on third-line TKI therapy and beyond, BCR::ABL1 TKD mutation testing result might be useful in case of low level mutations of unknown or doubtful clinical significance. Resampling should be performed after 1–3 months (depending on response level and kinetics).Sequential monitoring of a positive BCR::ABL1 TKD mutation testing result might also be useful if there is no improvement in response based on standard BCR::ABL1 transcript level monitoring after TKI switch.Sequential monitoring of a positive Whether a positive mutation result should lead to an immediate change of treatment depends on the level of non-optimal response, on the type and level of detected mutation(s) and on clinical considerations regarding the therapeutic alternatives available for each individual patient.BCR::ABL1. Bioinformatic analysis of NGS data should address this issue, taking all the necessary measures to minimize the likelihood of errors .Although reverse transcription and PCR sequence errors may always occur irrespective of the downstream method used to search for mutations and even with the use of high-fidelity enzymes, Sanger sequencing and digital PCR are less prone to false-positive results than NGS. Without a strategy of error correction , NGS artifacts are not infrequent, particularly at lower VAFs and/or lower levels of total 50 data are available for many mutations. Importantly, however, the cellular IC50 values do not always correlate with clinical response, and thus published clinical response data, if available, should always be considered. The possibility should also be considered that some rare sequence variants might be benign polymorphisms [Comprehensive databases of BCR::ABL1 TKD variants detected in TKI-resistant patients have not been developed, but ICorphisms  or that BCR::ABL1 transcripts in up to 70% of patients screened for mutations. By Sanger sequencing, its prevalence is much lower (1–2% of resistant patients) because of the inferior sensitivity of the method or because, even if present in >20% of the transcripts, overlap of sequences with and without the insertion in output chromatograms could be erroneously interpreted as background noise resulting from unincorporated fluorescent dyes. While initial reports had suggested a role for 35ins in TKI resistance, a subsequent functional study has convincingly demonstrated that the resulting truncated protein (the insertion introduces a stop codon after 10 intron-encoded residues) lacks kinase activity and does not show oncogenic properties [By NGS, insertions and deletions most probably resulting from alternative/abnormal splicing patterns are also a frequent finding. The so called ‘35ins’ variant for example, is the retention of 35 intronic nucleotides at the junction between exon 8 and 9 –152. By operties . ConsequMutations detected by Sanger sequencing or detected in ≥15% of transcripts by NGS or dPCR, should trigger consideration of an immediate change of therapy if they are recognized to be resistant to the ongoing TKI.IS levels or suspected/overt disease progression.Mutations detected in <15% of transcripts by NGS or digital PCR should trigger consideration of a change of therapy if they are recognized to confer resistance to the ongoing TKI and an immediate TKI change is deemed preferable over a wait-and-watch approach, for example in the context of continually increasing BCR::ABL1Mutations detected in <15% of transcripts that are not recognized as being recurrent in CML should be confirmed on an independent sample before being considered further. Mutations of unknown clinical relevance (i.e. never reported in the literature) should not trigger an immediate change of therapy unless the clinician deems the risks of continuing the ongoing TKI treatment to be much greater than the benefits .The ‘35ins’ variant should not be reported as a TKI-resistant mutation.BCR::ABL1 TKD mutation. Some of these may not be biologically driven and instead relate to non-adherence to therapy, which may be caused by a variety of reasons [JAK2 V617F and other MPN driver mutations. For reasons unknown, the co-occurrence of BCR::ABL1 and JAK2 V617F is significantly more common than would be expected by chance [BCR::ABL1-negative MDS or a related myeloid neoplasm [Approximately half of patients who relapse do so with no evidence of a secondary  reasons . Others  reasons , 67, 155y chance . In geneneoplasm , 157.


Crossfit athletes consistently recruit or transfer high levels of repetitive forces through the spine, and MRI has documented a higher rate of intervertebral disc degeneration in athletes compared with matched controls. The aim of this study was to evaluate early degenerative spinal disc changes in elite female CrossFit athletes quantified by 3.0 Tesla magnetic resonance imaging (MRI) matched with female none-athletes.In a cross-sectional single-center study 19 asymptomatic adult participants, nine German female elite Crossfit athletes and ten female participants underwent spinal MRI (3.0T). Demographic data, spinal clinical examination results and sport-specific performance parameters were collected prior to the MRI. The primary outcome was the prevalence of degenerative spinal disc changes. The secondary outcome was the grade of degeneration using Pfirrmann grading.A total of 437 discs underwent spinal MRI (3.0T). The prevalence of early degenerative disc disease was not increased. Pfirrmann degenerative grade did not show significant differences among groups.Asymptomatic female elite Crossfit athletes do not show an increased prevalence of degenerative disc disease. Compared to a sex-matched control group, high training volume in Crossfit does not correlate to a higher incidence of degenerative disc changes in young females. The findings of our study have the potential to significantly contribute to the field of sports medicine by shedding light on the relationship between Crossfit training and degenerative spinal pathologies in elite female athletes. The study shows that elite female Crossfit athletes do not experience increased spinal degeneration from high volume Crossfit training.Crossfit is a strength and conditioning program that combines various forms of high-intensity functional movements such as weightlifting, gymnastics, and endurance training to improve overall fitness. The workouts are performed in a group setting and are often intense and varied, with the goal of improving overall fitness and performance . AssociaIn contrast low back pain (LBP) is a common condition and symptom characterized by stiffness, pain, and discomfort in the lumbar region in society. 80% of adults report at least one episode of low back pain in their lifetime . Acute aThis creates an ambivalence between the fact that ambitious strength training can cause degenerative spinal pathologies and the fact that it is part of the therapeutic program of LBP.There is no clear consensus on whether Crossfit or weightlifting increases the risk of degenerative spine disease. Some studies suggest that heavy lifting and repetitive motions may increase the risk of back pain and spinal problems. Radiological changes have been identified in 84% of weightlifters with only few of them showing clinical correlation . Others Nowadays, more and more women are practicing Crossfit. To date, there have been no studies that address spine-specific degenerative problems in female high-performance Crossfit athletes. Since Crossfit, weightlifting, and powerlifting were mostly considered male-dominated, there is a growing importance of work that more closely examines gender-specific characteristics of various strength and conditioning disciplines.In a cross-sectional, single-center study between January and March 2023 national elite female Crossfit athletes were examined using subjective evaluation, clinical examination and MRI to provide information on degenerative disc pathology and degenerative spinal pathology.n = 9, age 26 ± 2.7 years, height 1.70 ± 0.07 m, weight 66.3 ± 7.18 kg), participating in the first national league (Fitness Bundesliga) 2022/2023 [n = 10, age 29.5 ± 2.9 years, height 1.70 ± 0.04 m, weight 61.5 ± 6.67 kg). Inclusion criteria were an elite fitness level defined through participation in the first German Fitness League 2022/2023, a nationwide elite Crossfit competition with eight match days throughout the year.A total of 19 female participants  took part. All patients were either female Crossfit elite athletes . The protocol included sagittal T1-weighted, T2-weighted, and short tau inversion recovery sequences for the cervical, thoracic, and lumbar spine. In addition, axial T1- and T2-weighted sequences were obtained for the cervical and lumbar spine. MRI examinations were performed by trained clinicians. The MRI examinations reviewed by radiologists experienced in the evaluation of orthopedic and traumatic spinal injuries and chronic spinal disorders, particularly disc degeneration, and were supervised by a board-certified neuroradiologist. The Pfirrmann classification system was used for the entire spine to grade disc degeneration, primarily in T2-weighted sequences . Demographic data, and sport-specific performance parameters are shown in Tables Distribution and severity of MRI findings are shown in Table One hundred fourteen intervertebral discs of the cervical spine were examined. Four discs were classified as grade I (3.5%), 64 discs as grade II (56.1%), and 44 discs as grade III (38.5%) according to Pfirrmann.n = 30, 55.6%; grade III: n = 21; 38.9%), and two discs showed no signs of degenerative changes. Degenerative changes occurred mainly in the upper cervical spine  and lower cervical spine (C5/C6). The control group had mild degenerative changes in the cervical spine in 59 of 60 discs . The differences in degenerative disc changes between the athletes and the control group were not significant , 219 discs as grade II (96.1%), one disc as grade III (0.4%), and 2 discs as grade IV (0.8%).n = 103; 95.4%; grade III: n = 1, 0.9%), three discs showed no signs of degeneration, and one disc showed moderate degenerative change. In comparison, the control group showed predominantly mild degenerative changes in 116 of 120 discs examined . One disc showed moderate degenerative change . Moderate degenerative changes were examined in the distal thoracic spine (T12/L1). The differences in degenerative disc changes between the athletes and the control group were not significant , 74 discs as grade II (77.1%), eleven discs as grade III (11.5%), and four as grade IV (4.2%).n = 39, 84.8%; grade III: n = 7, 15.2%) in the lumbar spine. One athlete had an additional sixth lumbar vertebral body, resulting in an extra disc. In the control group, 50 discs were examined. Forty discs showed mild degeneration , four discs showed moderate degeneration . The most severely affected discs were located in L4/L5 and L5/S1. The differences in degenerative disc changes between the athletes and the control group were not significant (Table Forty-six of 46 (100%) discs in elite female Crossfit athletes exhibited mild degeneration . In comparison, 70% of the control group had bulging or protrusion of the intervertebral discs. Pathology was observed in n = 10 discs . The differences were not statistically significant (p = 0.2105). In the lumbar spine, high intensity zones were observed in two athletes and five participants. Osteochondrosis intervertebralis was detected in one disc of one athlete (Modic type I) and in two discs of one participant in the control group (Modic type I). Spinal stenosis was found in 3 Crossfit athletes, 2 in the cervical spine and one in the lumbar spine. Due to the short pedicles aetiology of spinal stenosis was diagnosed as congenital. No spinal stenosis was present in the control group. The difference was not statistically significant (p = 0.0867). Facet joint arthropathy in the lumbar spine was found in 7 athletes and 7 participants in the control group. The differences were not statistically significant (p > 0.9999). No spondylolisthesis was present in our cohort. Vertebral endplate changes defined as Schmorl nodes, were observed in 17 vertebrae in five Crossfit athletes, predominantly in the thoracic and lumbar spine. In comparison, 20 vertebrae in 4 participants in the control group had Schmorl nodes. The differences were not statistically significant (p = 0.6563). MRI showed scoliosis of 11° to 25° in five athletes (55.6%). There was no scoliosis in the control group. The difference was significant (p = 0.0108). Further findings are listed in Table In addition, incidental findings for spinal degeneration were collected in all participants. All of the German female Crossfit elite athletes studied had an asymptomatic protrusion or bulging of at least one intervertebral disc. Protrusions or bulges of intervertebral discs were found in 16 discs, predominantly in the cervical and lumbar spine 3).There are several data on degenerative changes of the spine in competitive athletes , 10, 19.Regarding the cervical spine, almost all athletes in our study had some degree of disc degeneration, although they were asymptomatic. This contrasts with a study by Boden et al. who found that 25% of asymptomatic subjects under 40 years of age had some degree of cervical disc degeneration . CompareRadiological abnormalities of the thoracic and lumbar spine, a higher incidence of disc degeneration, and a direct association between disc degeneration and back pain have been found in various athletes , 9, 32. Currently, there is no clear consensus on whether Crossfit or weightlifting increases the risk of spinal degeneration. In contrast, acute spinal pathologies, particularly in the lumbar spine, are common during crossfit training, with radicular symptoms being the most common symptom . In a stThe results of our study show that degenerative disc changes on MRI are not necessarily associated with low back pain. Our results show degenerative findings in MRI in both examined groups but a clinical association with lower back pain is missing as both groups were asymptomatic. This is consistent with a study by Kasch et al. , who invLooking at the underlying pathomechanisms, mechanical stress cannot be considered as the only triggering factor for degeneration. Disc degeneration is a multifactorial process, mechanical factors, metabolism and nutrition, age and genetic factors are also involved in the development of the pathology , 35.The main limitations of this study are the small number of participating female athletes due to the narrow inclusion criteria, the complexity of the assessment, and the difficulty in recruiting elite female Crossfit athletes. The limited number of patients in both cohorts may not capture the full extent of the potential clinical and radiological effects of crossfit training on the spine. As a result, the study may not capture the magnitude of secondary effects and interactions. With this in mind, the data should be considered preliminary and further studies should be conducted.Further observations can be made when analyzing the MRI images of female Crossfit elite athletes compared to the control group. Considering that participants in our cohort would generally not receive an MRI in the absence of symptoms, anatomical differences can be observed after repeated high-intensity training Fig. .Future MRI studies of the spine, including both symptomatic and asymptomatic athletes, need to be conducted to improve understanding of disc disease pathology, associations with clinical degeneration symptoms, and effects of Crossfit training on the spine. This could lead to therapeutic strategies for patients with symptomatic degenerative spine disease.To date, there have been no studies that have addressed spine-specific problems in female Crossfit elite athletes. Our data show that the prevalence of early degenerative disc disease was not increased in asymptomatic female elite Crossfit athletes. No statistically significant differences were found when compared to a sex-matched control group, suggesting that high training volume in crossfit does not correlate to a higher incidence of degenerative disc changes in young female crossfit athletes.


One of the primary tasks for effective disaster relief after a catastrophic earthquake is robust communication. In this paper, we propose a simple logistic method based on two-parameter sets of geology and building structure for the failure prediction of the base stations in post-earthquake. Using the post-earthquake base station data in Sichuan, China, the prediction results are 96.7% and 90% for the two-parameter sets and all parameter sets, respectively, and 93.3% for the neural network method sets. The results show that the two-parameter method outweighs the whole parameter set logistic method and the neural network prediction and can effectively improve the accuracy of the prediction. The weight parameters of two-parameter set by the actual field data significantly show that the failure of base stations after earthquake is mainly due to the geological differences where the base stations are located. It can be envisioned that if the geological distribution between the earthquake source and the base station is parameterized, the multi-parameter sets logistic method can not only effectively solve the failure prediction after earthquakes and the evaluation of communication base stations under complex conditions, but also provide site selection evaluation for the construction of civil buildings and power grid towers in earthquake-prone areas. Bai et al.2 used a fault tree model to analyze the causes of service withdrawal of base station due to earthquake damage and obtained the failure probability of post-earthquake communication base stations. Li et al.3 derived the failure probability of post-earthquake communication base station rooms and towers and gave an assessment by conducting seismic vulnerability analysis of ground base station equipment and facilities. Thakur et al.4 selected rooftop towers of buildings with different slopes for dynamic analysis and comparison. Masoud et al.5 analyzed the seismic damage probability and vulnerability assessment of combined steel–concrete structures using finite element software. Agrawal et al.6 combined stochastic factors and real data to simulate earthquake occurrence and proposed a stochastic model to estimate the impact of seismic hazard on backbone optical network. To provide communication services to post-earthquake disaster areas, Peer et al.7 proposed a new multi-hop device-to-device (D2D) communication framework that connects devices which are not in the coverage area of the communication network to active base stations through relaying. Ibrah et al.8 designed a scheme to build an emergency relay network using multiple UAVs. Yao et al.9 designed an emergency wireless communication resource allocation scheme based on 5G-UAV. Li et al.10 proposed an air-ground integrated “Super-BTS” to form a dual routed transmission of local ring network + satellite transmission.Earthquake disasters can cause collapse of houses, damage to communication base stations towers and transmission lines, resulting in the disruption of communication services over a large area. The interruption of communication services prevents effective information exchange in the affected areas, which seriously hinders rescue and causes huge loss of life and property to local people. How to ensure communication after the earthquake is a key concern for disaster prevention and mitigation. In order to grasp the operation condition of post-earthquake communication base stations, Liu et al.11, building structure12, etc. and the earthquake. While ignoring that the damage of the post-earthquake communication base station is also related to many factors such as the geographical location of the base station, the distance from the earthquake source, the geography and geology between the earthquake source and the communication base station. Through the analysis of the causal relationship between the post-earthquake communication base station working conditions and these factors, a reasonable model is selected to make the siting of communication base stations in catastrophic earthquake-prone areas reliable, thus effectively reducing the failure rate of post-earthquake communication base stations. At present, there is no reliable method for siting and constructing communication base stations in earthquake-prone areas, and they are implemented according to standards, such as selecting seismic intensity according to geographical areas, there will be problems such as increased investment and failure of communication base stations after earthquakes. In fact, the sitting of communication base stations requires a specific analysis process, so that the causes of post-earthquake failure problems can be known, and relevant preparations can be made at the communication base station sitting stage.The above studies mainly analyzed the causes of failures based on the working conditions of post-earthquake communication base stations or propose a new emergency communication scheme. These new solutions are difficult and costly to implement in practice and are not conducive to post-earthquake disaster relief work. The post-earthquake communication base station condition analysis is limited to the relationship between the tower type of the base stationBased on the real operation data of post-earthquake communication base stations, this paper proposes a logistic method of parameter grouping, which can effectively evaluate the failure probability of post-earthquake communication base stations. And it can be used to judge whether the location of communication base stations is proper, to reduce the probability of failure of post-earthquake communication base stations, so that failure-free base stations can form the continuous coverage of emergency communication networks, and thus reducing the special investment in post-earthquake emergency networks.In the past, it was often thought that complex nonlinear methods such as artificial intelligence were needed to solve the problem of accurate prediction and evaluation of the working conditions of communication base stations after earthquakes, but in fact, traditional simple methods can improve the accuracy of prediction and evaluation just as well when more factors such as geology and geography and building structure are taken all into account. The advantage of traditional Logistic method compared to the method of innovative technology such as artificial intelligence is that the whole physical process is clear. Through the judgment of weighed-parameters of two-parameter set, it is derived clearly that geological factors have the greatest impact on communication base stations after earthquakes. In the construction of base stations, we can effectively reduce the construction cost of base stations by selecting the location of base stations reasonably rather than seismic intensity level.To avoid the accidental source of post-earthquake communication base station data in a single region, it is necessary to select post-earthquake communication base station data from different earthquake magnitudes, different earthquake fault zones, different geological and geographical environments, and other composite factors when selecting experimental data. In this paper, the post-earthquake communication base station statistics of three earthquakes, Wenchuan Ms8.0 earthquake, Lushan Ms7.0 earthquake and Changning Ms6.0 earthquake in Sichuan Province, are used as the object of analysis and research. The geographical contour maps of the seismic regions are shown in Figs. Figure Lushan County is a district and county under Ya’an City. As can be seen in Fig. Changning is a county of Yibin, located in the southeast of Yibin City and belongs to Huayingshan fault zone. As can be seen from Fig. 3. Therefore, scenarios can be classified based on the above description of the post-earthquake base station room condition and tower condition, normal or damaged is selected as the measurement criteria.Although all three earthquakes occurred in Sichuan Province, China, they were in different areas with different magnitudes and seismic intensities. In the post-earthquake survey, it was found that the communication base stations could maintain basic operation if the main equipment such as power supply system, wireless equipment and transmission equipment were not damaged after the earthquake, and the towers played the role of antenna support and information transmission without changes; if any of them were damaged, the communication base stations could not operate effectively14. The occurrence of earthquakes is on seismic fault zones, which are directional in nature, and the communication base stations are more severely damaged in the area towards the seismic fault zone, and the seismic intensity is higher in this direction, which requires the selection of the Base station relative epicentral position in the selection of parameters.The impact of earthquakes on communication base stations can be divided into geological factors underground and environmental factors above ground. The data in this article is from Sichuan Province, China. Sichuan Province is a mountainous area, and the lithology of the area is mainly sedimentary, magmatic and metamorphic rocks, so we use these three rocks as a description of geological structure. The above-ground environmental factors include the geographical location of the communication base station construction and the construction type of the communication base stationAccording to the above definition of variable parameters that affect the working conditions of the base station, the parameters can be divided into local variable parameters and global variable parameters. Global variables have global scope, which means that all communication base stations in the affected area are affected by the same objective factors at the time of an earthquake, such as magnitude, seismic intensity, geological structure, and the relative orientation of the base station to the epicenter. Local variables only have a local scope, which are affected by the geological and geographical characteristics of the communication base station location, the structure of the base station and other factors, such as magnitude, seismic intensity, geological structure, geographical location of the base station, base station category, computer room situation, tower type, etc. Earthquake magnitude and seismic intensity are the direct factors affecting the working conditions of post-earthquake communication base stations, so the two divisions of local variable parameters and global variable parameters overlap in magnitude and seismic intensity.X, and whether the base station operates after the earthquake is set as output variable Y.Due to the enormous number of influencing factors, the small amount of data, and the significant differences between local factors such as geological changes and seismic grade, a two-parameter set consisting of global and local parameters was modeled, and the results were derived from the weighted sum of the local and global parameters estimated through binary regression. The local parameters and global parameters are shown in Tables VIF is used to detect covariance in the data15. When 0 < VIF < 10, there is no collinearity. The VIF value between the actual data is less than 2, there is no collinearity in the data set. The results are shown in Table If collinearity occurs in the quantified data, it will seriously affect the inference and prediction in regression analysis. In this paper, the variance inflation factor 15. There are two sets of experimental data outliers on the 95% confidence interval, and the outlier data were removed. The results are shown in Fig. To avoid outliers in the data set from affecting the results of regression analysis, standardized residuals were used for testingThe problem of different nature and comparability of data is solved by using data normalization to unify the comprehensive variables to the same standard through homogenization and dimensionless processing of multiple variables parameters in the data set after eliminating outliers. In this paper, 80 sets of experimental data were finally selected and randomly divided into training and test sets in the ratio of 7:3 for experiments.20 with an autonomous learning function that can find the optimal solution at high speed by continuous feature learning in the face of complex environmental problems is also introduced for comparison. The neural network model in this paper is a three-layer structure, with the first layer as the input layer, taking the variable parameters as the input; the second layer as the hidden layer, responsible for weighting the data; and the third layer as the binary output layer, deciding the base station operating conditions based on the output. The coefficients of the hidden layer of the neural network are adjusted by the backward propagation algorithm to complete the model training.In this subsection, the logistic model and the two-parameter set model are used to predict the working condition of the post-earthquake base station respectively. In view of the dynamic changes in the location of the earthquake source and the operating working condition of the communication base station, a neural network model P > 0.4 and 0 when P < 0.4.In the experiment, the default classification threshold of the binary classification model is 0.5, which is not reasonable in specific scenarios. In this paper, the method is applied to practical engineering, and the threshold value is selected according to the maximum prediction correct rate in the training set results. The distribution of the prediction probability of post-earthquake communication base station conditions in the training set is analyzed; the correct prediction rate is the maximum when the threshold is set to 0.4. According to the training set training results, the result is judged as 1 when the output prediction probability valueTable a and b, where a is the local parameter weight and b is the global parameter weight.Table Several factors affecting the post-earthquake communication base station operating conditions are modeled with non-grouping and grouping parameters, respectively. Based on the grouping results, the probability weighted accumulation obtained by conducting evaluation prediction using the two classification parameters obtained from regression modeling is jointly evaluated to predict the post-earthquake base station operating conditions. As shown in Table The method of parameter grouping can clearly present the relationship between the grouped parameters. The weight of the global parameter obtained from the field data processing is 2.2 times of the weight of the local parameter, showing that the influence of the geological parameter and the base station relative epicentral position in the global parameter is huge, showing that the choice of the base station construction related to geological factors is important. The non-grouping parameters do not reflect the role played by a specific parameter among the influencing factors, while in the grouping parameters we can indicate which parameter is more important by the weights. By this method it is possible to consider geology as the first factor in the failure prediction of post-earthquake communication base stations or in the choice of future base station construction sites.In this study, we found that in addition to the traditionally perceived factors such as magnitude and seismic intensity, base station relative epicentral position and geological geography are also factors that affect the post-earthquake communication base station working conditions, among which geological factors are the most important. Unlike the previous methods for predicting the post-earthquake base station working conditions, the two-parameter method of grouping these influencing factors can extract useful information of the influencing factors to a greater extent and improve the correct prediction rate in a complex geographical background environment using a smaller number of base stations.25.The operating condition of a post-earthquake base station can be represented by a binary classification, i.e., 1 is operation and 0 is fault, so the prediction and evaluation of its operating condition is a binary classification problem. Therefore, a binary classification (0 or 1) method is needed to estimate the operating condition of the post-earthquake base station. Logistic regression is a method based on linear regression theory, which can effectively deal with the binary classification (0 or 1) problem with distinct characteristics by introducing nonlinear factors through the sigmoid function. It is often used in data mining, automatic disease diagnosis, economic forecasting and other fieldsThe essence of logistic regression is to divide the probability of occurrence by the probability of non-occurrence, and then take logarithm, so that a linear relationship is formed between dependent variables and independent variables. The logistic model is set up as follows:The above formula can visually present the probability of dependent variable after transformation:Due to the limited actual data of post-earthquake communication base stations, the amount of data in this paper has just reached the requirement of logistic regression analysis, i.e., the amount of data is 10 times of the variable parameters. It is unreasonable to process the regression parameters by data from only a single regional earthquake source, and the accuracy of the obtained results may not be high, so it is necessary to analyze and figure out the distribution of regression parameters using random combinations of data from different earthquake sources. To avoid the low prediction accuracy caused by the small amount of data, this study proposes a two-parameter set method based on logistic regression to improve the prediction accuracy, and the processing flow of the post-earthquake base station failure probability with two-parameter set is given in Fig. The communication base station data from different seismic sources are randomly combined and randomly divided into training set and test set according to the ratio of 7:3. 70% of the training set data are used for learning and 30% of the test set data are used for testing.Step 1, the first set of base station data is randomly selected in the training set, and the base station data is divided into the set of global variable parameters and local variable parameters. Perform logistic regression tests on these two parameters sets to obtain the prediction probabilities of the global variable parameters and the prediction probabilities of the local variable parameters.Step 2, the second set of base station data is randomly selected in the training set, repeat the first step, …, until the logistic parameter regression test is completed for all finite data combinations, and obtain the prediction probabilities of all global variable parameters and local variable parameters in the training set.Z, and the post-earthquake base station conditions of normal and failure as 1 or 0, respectively, with the weights set to a and b, and the prediction probabilities obtained from the local and global parameters of the model are set to x and y, respectively, c as constants. One communication base station corresponds to a binary equation, and the equation for post-earthquake communication base station condition prediction is set up according to the number of actual test data, the weights a and b can be obtained by the principal component analysis method15, see Eq. (The weights are obtained by using the post-earthquake base station conditions of the test data, giving the post-earthquake base station conditions as  see Eq. .3\documea and b were obtained using principal component analysis in three steps:The weights  k.The first step is to determine the composite score of local parameters. Firstly, the selected local variable parameters are subjected to principal component analysis to obtain the corresponding principal components. Then, the composite score model is calculated from the variable coefficients, component contribution rates and cumulative contribution rates of the principal components, and finally, the selected local parameter data are brought into the above local composite score model to obtain the composite score of the base station data, which is denoted asg.The second step determines the composite score of global parameters. In the same way as the local parameter weights are decided; the composite score of the base station data is calculated and denoted as k derived in the first step and the g derived in the second step, the weights a and b are calculated for the local and global parameters, respectively.The third step is to determine the weight. Using the 24.Based on the grouped prediction probabilities from the two-parameter group regression, a weighted accumulation of grouped probability values is performed to obtain the final prediction evaluation probabilitiesThrough the above learning process, the weight parameters of the model can be obtained, and the weight parameters of the model can be used to predict the post-earthquake conditions of 30% of the test set of base station data once they are determined.In this method, the geological structure, geographic location of the base station, and the category of the base station in the parameter variables are objectively available when evaluating the siting of communication base stations in earthquake-prone areas. Base station relative epicentral position can be determined according to the preset epicenter, the earthquake magnitude and seismic intensity are preset according to human needs.m and n, respectively, and seismic intensity and magnitude are set to e as a constant.However, the condition of the base station room and the tower, which are also input parameters, are unknown. The joint solution of seismic magnitude and seismic intensity is used to judge the seismic capability of the communication base station infrastructure. The logic given to the post-earthquake condition of the communication base station infrastructure is H, and its normal and failure are 1 or 0, respectively. The weights of seismic intensity and magnitude are set to A binary linear system of equations is set up, with g to Eq. , and the


Reputation is a vital asset for Higher Education Institutions (HEIs) as it constitutes a source of competitive advantage because it works as a quality sign for the stakeholders. Because of globalisation, decreasing funding and the changing environment in the Mozambican higher education market, HEIs need to build a favourable reputation to stay relevant. This research aims to study how a university’s reputation can influence student loyalty.Utilizing a descriptive and analytical methodology, a quantitative investigation was carried out by surveying 402 students enrolled in higher education institution (HEI) courses in Mozambique. The research employed a survey questionnaire to directly collect information from the study participants.The study concluded that university reputation affects student loyalty, as the relationship between both variables is statistically significant. All dimensions of HEIs reputation, namely, academic competence, social attractiveness, and responsible management, positively impact student loyalty. Although, the latter variable did not have a statistically significant impact, contradicting the current theoretical framework.The study’s findings suggest that corporate reputation has a favorable impact on student loyalty, demonstrating a significant relationship between the two factors. Therefore,improve student retention, HEIs should seek to enhance their academic competence and social attractiveness.The study suggests future research should address sample composition issues by increasing representation from underrepresented groups. It recommends a more in-depth exploration of the responsible management construct, advocating for the inclusion of context-specific indicators. Furthermore, the study proposes investigating additional factors to enhance the understanding of the relationship between university reputation and student loyalty. In a globalised and increasingly competitive market characterised by increased student mobility and reduced government funding, higher education institutions (HEIs) must compete to attract the best students . TherefoAn institution’s reputation has to do with the reliability and credibility that an organisation projects to its stakeholders , and repIt is acknowledged that corporate social responsibility, as organizations actions that considers stakeholders, impacts positively in brand reputation of higher education institutions . CSR praReputation is essential for organisations, in general, as it constitutes a strategic resource with the potential to create value  and to oThe research agenda in this area of knowledge recommends that further studies be carried out in different contexts , as mostBesides the fulfilment of the contextual gap, this paper will contribute to the academic debate by yielding a deeper understanding of which factors are more determinant when managing the university’s reputation to improve student loyalty. Furthermore, the study aims to compare perceptions of students between private and public universities, as suggested in s previous study bySimilar to other countries, the landscape of the HEIs market in Mozambique has changed dramatically in the late years, with the decrease in public funding and a rapid expansion of the number of institutions and student population, resulting in an overcrowded market in the private and public sectors. This rapid transformation was seen by the public opinion with significant concerns about neglecting the educational quality, consequently hindering the image and reputation of the whole country’s tertiary education system. Additionally, in 2012, the approval of the new financing strategy for HEIs in Mozambique, which provides for the financing of higher education institutions based on performance indicators , comes w•Which dimensions of university reputation are determinants of student loyalty?•Do university reputation and student loyalty differ between public and privately owned institutions?Thus, this study wishes to answer the following questions:Knowing how university reputation affects student loyalty and what factors are more critical is relevant because HEIs can focus on improving the dimensions essential for their success, depending on the context. The research will highlight that some dimensions may not be as relevant as expected.Corporate reputation has been systematically and more frequently the subject of debates and discussions , hence tReputation is a multidisciplinary concept, so there are several definitions with different perspectives in the literature .Reputation in an HEI can be defined simply as ‘the collective representation maintained over time by the multiple constituents  of a university’  inStudent loyalty has attached to its measurement the following indicators: the intention to choose the same university, the recommendation to others and the intention to leave the university when possible . BlackmoA favourable corporate image and reputation influence the attitudes of the organisation’s stakeholders . Among oH1: Corporate reputation has a significant relationship with student loyalty.University reputation for this research contains three main dimensions: academic competence, social attractiveness and responsible management . For thiPrior studies have shown that the quality of education an institution provides , and graH2: Academic competence has a significant relationship with student loyalty.The quality of the facilities  such as H3: Social attractiveness has a significant relationship with student loyalty.The reputation of the institution’s top management is one of the critical elements of HEIs reputation management. This factor helps to establish good relationships with both internal and external stakeholders, reinforcing their confidence in the university’s administration . Thus, tH4: Responsible management has a significant relationship with student loyalty.Strategically, public HEIs increasingly mirror private sector practices . HoweverStudent perceptions and assessments about their HEI may vary across public and private institutions  due to sH5: There is a significant difference in the level of corporate reputation between public and private HEIs.H6: There is a significant difference in student loyalty between public and private HEIs.SeeThe purpose of this study is to find out and explain the relationship between university reputations of Mozambican public and private institutions and student loyalty. The research design of the study is descriptive and analytical. The research is based on a quantitative survey and analysis. The main instrument for data collection was a closed-question questionnaire consisting of items from the scales of the primary constructs of the study, namely corporate reputation and student loyalty. The items on the scales are measured by a five-point Likert scale, which has the following description, strongly disagree, tend to disagree, neutral, tend to agree, and strongly agree. Respondents provided written informed consent to fill the questionnaire voluntarily, and anonymity and confidentiality were granted to comply with ethical issues. A scientific board approved the ethical compliance of the study, and the author presented the project for approval before a jury, in 2019, as part of his PhD thesis.The Scientific Commission of The Faculty of Economics of UEM approved the ethical compliance of the study .The unit of analysis of this study is public and private higher education institutions operating in Mozambique. The sample unit is students currently attending a course at a higher education institution in the country. The sampling technique was convenience and snowball, and the study’s sample size was 384 respondents. However, 402 questionnaires were collected with 396 valid responses.The data was collected online through an electronic survey platform from August to October 2021, where students attending different HEIs from all the regions of Mozambique filled out the questionnaire. The researcher reached out to universities, professors, and university students, providing a link to individuals who met the sample criteria, asking them to fill out the questionnaire and distribute it to others with similar profiles. As the author is a university faculty member, the snowball sampling method was combined with convenience sampling by also surveying students from the author’s institution.For data analysis and testing, SPSS version 20 was used. The collected data were subjected to a series of statistical tests, such as multivariate regression, bivariate analysis, ANOVA test, and exploratory factor analysis.The sample has a slightly higher male representation (50.5%), with the most expressive age group being 21 years old or less with 41%, slightly above the 22-30 age group, representing 40.8%. Regarding educational establishments, 82% of respondents belong to public institutions. Universities represent 88.4%, followed by Higher Institutes with 10.4%. The first three years concentrate a higher proportion of students (62.8%). SeeThe original authors tested already the indicators of the scales of the main constructs of the present study and presented a high index of reliability measured through Cronbach’s Alpha. However, to reinforce the validity of the scales in the Mozambican context, the author tested the reliability of the constructs, and the results showed that all constructs have good reliability. SeeH1: Corporate reputation has a significant relationship with student loyalty.2) the variation of the dependent variable . Therefore, the model manages to explain almost half of the dependent variable, leaving a significant part of 55% explained by other variables. The hypothesis of R2 being zero being statistically significant (F(3.391)=109,627; p<0.001) is rejected.The results show that the independent variables  explain in 45%  show a statistically significant correlation (p<0.00) with the independent variable . Correlations range from moderate for Responsible management (0.442) and Social attractiveness (0.588) to high for Academic competence (0.63). The coefficient table shows the equation of the estimated line which is: Student Loyalty=-0.187+0.622 Academic competence+0.388 Social attractiveness+0.032 Responsible management.H2: Academic competence has a significant relationship with student loyalty.H3: Social attractiveness has a significant relationship with student loyalty.Academic competence (p<0.001) and social attractiveness (p<0.001) have both a high significance in the dependent variable. The results confirmed the hypotheses positively.H4: Responsible management has a significant relationship with student loyaltyThe variable responsible management, in addition to having a shallow impact on the dependent variable (H4), that is, in the model, not an explanatory determinant of student loyalty, and this relationship is not statistically significant (p=0.629). Thus, H4 was not verified.H5: There is a significant difference in the level of corporate reputation between public and private educational institutions.In public (3.71) and private (3.74) institutions, corporate reputation is higher than the centre of the scale, slightly higher in private institutions. The difference in means is not statistically significant (H5) (t(393) =-0.327; p=0.326). Students from private institutions believe their institutions have a slightly higher reputation. A slightly higher percentage (38.2%) believe that the reputation level of their institutions is high. In comparison, only 37% of students from public higher education institutions say the same. The hypothesis H5 was not validated.H6: There is a significant difference in the level of student loyalty between public and private educational institutions.As for the level of student loyalty, the results show that in both public and private institutions, student loyalty is higher at the centre of the scale, being slightly higher in public institutions. The difference in means is statistically significant (H6) (t(87793)=2,373; p=0.04). Students from public institutions showed more loyalty towards their institutions. A higher percentage (48%) of students from public institutions claimed to be very loyal. In contrast, the percentage of students from private institutions said the same was 36.8%. The hypothesis was confirmed .This study aimed to investigate the influence of university reputation and its dimensions, namely academic competence, social attractiveness and responsible management, on student loyalty. The research targeted student loyalty as a dependent variable as previous studies proved that loyalty plays a crucial role in creating the success of HEIs, which is why it requires special attention and resources. This variable impacts increasing the funding of institutions through high retention rates and lasting relationships and synergies through positive recommendations, by students and other stakeholders, about the institution .The study’s empirical evidence showed a positive and significant relationship between university reputation and student loyalty in the context of HEIs in Mozambique, both for public and private institutions. The results of the study confirmed previous thoughts that the reputation of an HEI helps to increase student loyalty, positively affecting their intentions to collaborate with the institution in the future . Thus, rThe study also confirmed previous knowledge that academic competence and social attractiveness are crucial in influencing student loyalty, as the regression analysis demonstrated a significant relationship between those dimensions and the dependent variable. When students perceive that the HEIs provide them with quality education and a robust physical and social infrastructure, they tend to be more loyal to the institution  of the variance of the dependent variable, student loyalty. The relationship between the two variables is significant. The dimensions of corporate reputation with the most impact are academic competence and social attractiveness. The results showed that responsible management has a residual and non-significant impact on student loyalty.The study concluded that corporate reputation positively affects student loyalty. Corporate reputation can explain 45%  and students enrolled in Academies, Colleges and Superior Institutes (11.7%). Thus, for future research, to generalise the results with more confidence, it is recommended to increase the proportion of representatives of those groups, as it might yield richer insights.Secondly, bearing in mind that the responsible management construct was the one that presented the lowest degree of reliability of Cronbach’s Alpha (0.71), it is recommended that for future research, a deeper exploration of the construct with the inclusion of other indicators that reflect and adapt to the local context. Previous research (2) of the variance of the dependent variable. The results showed no significant differences in reputation between public and private universities. While regarding student loyalty, the result presented significant differences between both groups. This finding may suggest that there are other variables intervening in the model. Thus, to better understand the mechanisms of the relationship between university reputation on student loyalty, other variables such as service quality  in the context of HEIs. It could benefit from a sentence or two explaining how CSR specifically ties into the reputation and competitive advantage of HEIs. The introduction could benefit from a clearer explanation of how Corporate Social Responsibility (CSR) specifically influences the reputation of higher education institutions. While the introduction discusses reputation at length, the direct impact of CSR initiatives on reputation and student loyalty is not explicitly stated. The author should consider detailing the mechanisms through which CSR practices contribute to the reputation of HEIs, potentially influencing student perceptions and decisions, and how this might differ in the context of Mozambique compared to other regions. While the study highlights the significant impact of academic competence and social attractiveness on student loyalty, it would be beneficial to discuss the implications of responsible management's non-significant impact. The author could speculate on why this might be the case and what it could mean for HEIs' CSR strategies. The author notes that both corporate reputation and student loyalty are above average, but does not elaborate on the implications of this finding. It would be insightful to discuss whether this is consistent with expectations and how it compares to other studies in similar contexts. Address the Unique Context of Mozambique: The recommendations are somewhat generic. The author could provide more tailored advice for Mozambican HEIs, considering the country's specific educational, economic, and cultural context.Is the work clearly and accurately presented and does it cite the current literature?YesIf applicable, is the statistical analysis and its interpretation appropriate?YesAre all the source data underlying the results available to ensure full reproducibility?YesIs the study design appropriate and is the work technically sound?YesAre the conclusions drawn adequately supported by the results?PartlyAre sufficient details of methods and analysis provided to allow replication by others?PartlyReviewer Expertise:Communication Strategies / Branding / CSRI confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Starting from the consideration the relevance of the topic discussed, namely, the impact of reputation on student loyalty in the framework of Higher Education. This is a topic of interest that is addressed in this specific case in a geographical context, Mozambique, where there does not seem to be any research on the matter. Therefore, one of the essential contributions of the work is just this: covering the research gap in Africa context.Which dimensions of university reputation are determinants of student loyalty?Do university reputation and student loyalty differ between public and privately owned institutions? Besides, the answer to these questions allows us to address the implications for reputation management in universities. The research questions are relevant and well focused.  The strategic importance of image and reputation of organisations is widely recognized. However, despite the extensive body of knowledge generated around the concept of reputation in organisations, researchers continue to underscore the existence of important conceptual inconsistencies surrounding them. In this paper, the author recognizes that reputation is a multidisciplinary concept but assumes a definition as a valid definition. The lack of conceptual clarity is one of the most significant research gap, so I would like to read a better argumentation about why the author has chosen Alessandri et al as the best definition of reputation.Forcada, J: A Review of Higher Education Image and Reputation Literature: Knowledge Gaps and Research Agenda. Eur. Res. Manag. Bus. Econ. 2017; 282: 1043–1064. The following quote contains errors in one of names:  Lafuente-Ruiz-de-Sabando A, Zorrilla P, There are problems in several of the links to the articles cited in the bibliography.Is the work clearly and accurately presented and does it cite the current literature?YesIf applicable, is the statistical analysis and its interpretation appropriate?PartlyAre all the source data underlying the results available to ensure full reproducibility?YesIs the study design appropriate and is the work technically sound?YesAre the conclusions drawn adequately supported by the results?YesAre sufficient details of methods and analysis provided to allow replication by others?YesReviewer Expertise:MarketingI confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. This paper is related to the current issue especially in the effort to increase number of student and keep them retain on the same university. Abstract- please include briefly on future research. Introduction - please clearly indicate the impact of poor reputation toward student loyalty.Literature review - please consider to more reference for higher TVET institution as well.  Prior to the developing of hypothesis, please clearly relate this study with related underpinning theory. In addition, need to ensure that the hypothesis is developed based on past research. Method - sampling method needs to clearly defined. Result- need to clearly explain the current performance.Discussion - adequate. However, better to have supporting docs.  Perhaps, please consider the following input for improvement: Is the work clearly and accurately presented and does it cite the current literature?YesIf applicable, is the statistical analysis and its interpretation appropriate?YesAre all the source data underlying the results available to ensure full reproducibility?YesIs the study design appropriate and is the work technically sound?YesAre the conclusions drawn adequately supported by the results?YesAre sufficient details of methods and analysis provided to allow replication by others?YesReviewer Expertise:Quality Management System, Operation Management, Supply Chain Management, Service Quality, TVET Education studies.I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.