Hepatoma Res. HR Hepatoma Research 2454-2520 OAE Publishing Inc. 10.20517/2394-5079.2026.42 Review Malnutrition in MASLD-related hepatocellular carcinoma: assessment limitations, multidimensional mechanisms, and microbiome-based interventions Huang Baizhen 1 Tian Chunbo 2 Sui Yutong 1 1Department of Traditional Chinese Medicine, the Shenzhen Hospital of Southern Medical University, Shenzhen 518000, Guangdong, China. 2Administrative office, Heilongjiang Provincial Hospital, Harbin 150000, Heilongjiang, China. Correspondence to: Dr. Yu-Tong Sui, Department of Traditional Chinese Medicine, the Shenzhen Hospital of Southern Medical University, Shenzhen 518000, Guangdong, China. E-mail: hljzysyt@126.com

Received: 14 Apr 2026 | First Decision: 9 May 2026 | Revised: 28 May 2026 | Accepted: 23 Jun 2026 | Published: 10 Jul 2026

Academic Editor: Stefano Bellentani | Copy Editor: Ting-Ting Hu | Production Editor: Ting-Ting Hu

 2026 10 7 2026 12 37 © The Author(s) 2026. © The Author(s) 2026. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, sharing, adaptation, distribution and reproduction in any medium or format, for any purpose, even commercially, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

With the rapid rise in hepatocellular carcinoma (HCC) incidence associated with metabolic dysfunction-associated steatotic liver disease (MASLD), malnutrition has emerged as a critical comorbidity that affects treatment tolerance and survival outcomes. This review systematically examines the challenges of assessing malnutrition in metabolic dysfunction-associated steatotic liver disease-related hepatocellular carcinoma (MASLD-HCC), its multidimensional pathological mechanisms, and emerging intervention strategies. In current clinical practice, the absence of specialized assessment tools limits the effectiveness of existing evaluation systems in addressing distinct phenotypes, such as obesity and ascites. The underlying mechanisms are complex; disruptions of the “gut-liver-muscle axis”, centered on intestinal dysbiosis, form a key network driving metabolic reprogramming, chronic inflammation, and muscle wasting. Regarding interventions, microbiome-targeted therapies - including fecal microbiota transplantation and probiotics/synbiotics - have shown promise in preclinical studies for modulating immunometabolism and improving nutritional status. However, their translation into routine clinical practice remains constrained by major bottlenecks, including insufficient large-scale randomized controlled trial evidence, uncertainty regarding long-term safety, and inadequate standardization. Future efforts should urgently focus on establishing precise assessment systems and conducting high-quality clinical validation.

 Hepatocellular carcinoma MASLD-HCC malnutrition assessment gut-liver-muscle axis microbiome-targeted therapy INTRODUCTION

Metabolic dysfunction-associated steatotic liver disease (MASLD) has emerged as one of the leading etiological factors for hepatocellular carcinoma (HCC) worldwide. Based on data from the past three decades, the contribution of traditional dominant factors (viral hepatitis and alcohol) has gradually declined, whereas the proportion of cases attributable to MASLD has increased annually by 0.66% (95%CI: 0.64%-0.67%)[1], consistent with previous findings[2,3].

Malnutrition is a common complication of HCC, with a reported prevalence of approximately 25% among HCC patients[4,5]. It is directly associated with unfavorable treatment outcomes, reduced quality of life, and increased mortality in HCC[6,7]. The mechanisms contributing to malnutrition in MASLD-related HCC may include insulin resistance (IR), metabolic energy reprogramming, chronic inflammation, gut-liver axis dysregulation, and anorexia-associated behavioral changes[8-10].

At present, there is a lack of screening and assessment tools specifically designed for metabolic dysfunction-associated steatotic liver disease-related hepatocellular carcinoma (MASLD-HCC) in routine clinical practice, and general tools such as Nutritional Risk Screening 2002 (NRS-2002) and the Patient-Generated Subjective Global Assessment (PG-SGA) remain widely used[11]. However, these tools have limitations in accurately evaluating specific indicators, including obesity phenotype, fluid retention, and hypoalbuminemia, which results in a high risk of underdiagnosis. To improve assessment accuracy, clinical practice may consider combined strategies, such as incorporating the Royal Free Hospital-Nutritional Prioritizing Tool (RFH-NPT), integrating imaging examinations or anthropometric measurements, and including inflammatory biomarkers[12-14].

Current nutritional support strategies primarily focus on improving clinical indicators and alleviating patient discomfort[15]. Microbiota-targeted therapies, such as probiotics and fecal microbiota transplantation (FMT), may provide new perspectives, although large-scale clinical validation is still lacking. This article offers a concise overview of existing nutritional assessment tools, pathogenic mechanisms, and intervention strategies for malnutrition in MASLD-HCC, with the goal of informing clinical practice and guiding future research.

Currently, there are no specific nutritional screening and assessment tools designed for patients with MASLD-HCC in routine clinical practice. For malnutrition risk screening in adult oncology outpatients, the American Society for Parenteral and Enteral Nutrition (ASPEN) has classified tools into different levels according to usability: the Malnutrition Screening Tool (MST) is classified as Level 1, the PG-SGA as Level 2, and the Malnutrition Universal Screening Tool (MUST) and the NRS-2002 as Level 3[16]. However, Ge et al. reported that although MST is the fastest, its sensitivity is too low (28.5%), and it is recommended only for community settings[17]. The scored Patient-Generated Subjective Global Assessment (sPG-SGA) has been established as a standard nutritional assessment method for cancer patients and is widely used in clinical practice[18]. In a retrospective analysis of 3,777 adult hospitalized cancer patients, the GLIM criteria showed moderate agreement with sPG-SGA[19]. Another study found that, without initial screening using NRS-2002, GLIM demonstrated greater concordance with PG-SGA for diagnosing malnutrition[20]. In addition, for elderly cancer patients, the Mini Nutritional Assessment Short Form (MNA-SF) has also shown good applicability[21].

However, none of the tools above are specifically designed for HCC patients. HCC is frequently accompanied by sodium and water retention and hypoalbuminemia; ascites may therefore mask weight loss related to muscle or fat loss, potentially compromising the accurate interpretation of existing tools. In contrast, screening tools designed specifically for malnutrition in liver cirrhosis partly address this limitation. Clinical studies have shown that the Liver Disease Undernutrition Screening Tool (LDUST) appears to be more accurate than MUST and NRS-2002 in assessing nutritional status in patients with ascites[22]. The 2019 European Society for Clinical Nutrition and Metabolism (ESPEN) guidelines also recommend using the Royal Free Hospital Nutritional Prioritizing Tool (RFH-NPT) for nutritional screening[15]. Although RFH-NPT has drawbacks - such as a longer administration time and the requirement for a professional dietitian to be involved[23] - other studies have indicated that RFH-NPT and LDUST are the only screening tools proven to accurately detect malnutrition in patients with cirrhosis[24].

With a focus on MASLD-HCC, one of its important features is sarcopenic obesity[25]. Assessment tools that rely on low body mass index (BMI) for diagnosis may be biased. A study using the Controlling Nutritional Status (CONUT) index as the assessment standard found no significant difference in the prevalence of malnutrition between obese (BMI ≥ 30) and non-obese MASLD patients[25]. In terms of body composition assessment, the skeletal muscle index at the third lumbar vertebra measured by CT is considered the gold standard, whereas the psoas muscle thickness-to-height ratio (PMTH) offers advantages in simplicity and speed[26].

In summary, there is currently no specific screening and assessment tool for nutritional status tailored to a single type of cancer in routine clinical practice. Combining different tools is therefore necessary to improve diagnostic accuracy. For example, combining RFH-NPT with mid-upper arm circumference (MUAC) or mid-upper arm muscle circumference (MUMC) can significantly improve the diagnosis of sarcopenia[27]. Artificial intelligence is promising for integrating multimodal data and enabling dynamic prediction, but it currently lacks support from large-scale datasets[28]. For details, see Table 1.

Different screening and assessment tools

Screening tool Reference standard Sample source Assessment dimensions Sensitivity Specificity AUC Ref. Advantages Limitations
GLIM sPG-SGA 3,777 adult cancer inpatients 1. At least one phenotypic criterion and one etiologic criterion should be combined 70.5% 88.3% 0.79 [19] Internationally unified, dual-dimensional (phenotypic + etiologic), compatible with existing screening tools, and highlights sarcopenia Relatively complex, and some items may be influenced by subjective factors
PG-SGA GLIM 637 adult cancer patients 1. The patient-completed component includes, specifically, weight loss, nutrition-impact symptoms, dietary intake, and functional capacity2. The clinician-completed component assesses disease and age, metabolic stress, and physical examination 81.1% 71.6% 0.763 [29] Preferred in oncology settings, with good predictive performance Time-consuming and requires trained personnel
NRS-2002 GLIM 637 adult cancer patients 1. BMI2. Dietary intake3. Disease severity 81.7% 97.6% 0.896 [29] Content and predictive validity: moderately reliable; practical; considers disease severity Weight from fluid collections (ascites, peripheral edema) is not accounted for
MUST GLIM 1. BMI2. Unintentional weight loss and acute disease compromising nutritional intake for more than 5 days 52.2% 99.1% 0.757 Validated in both inpatients and outpatients; rapid and simple Weight from fluid collections (ascites, peripheral edema) is not accounted for, and disease severity is not considered
MNA-SF GLIM Forty cancer patients (mean age 84.8 ± 5.5 years) 1. BMI2. Recent weight loss3. Appetite changes4. Mobility5. Psychological stress and neuropsychological problems 100% 50% 0.75 [21] Simple, non-invasive, and economical Primarily targeted at elderly patients
AIWW PG-SGA 11,360 cancer patients 1. Age2. Dietary intake3. Weight loss4. Walking ability 93.40% 46.10% 0.785 [17] Very high sensitivity with rapid completion Low specificity; requires validation with GLIM
RFH-NPT GLIM 78 patients with liver cirrhosis 1. Alcoholic hepatitis or tube feeding2. Fluid overload and its impact on food intake and weight loss3. BMI, unplanned weight loss, and daily dietary intake 90.91% 60.00% 0.755 (0.646-0.863) [30] A liver disease-specific tool; rapid and simple; reduces the impact of fluid retention Requires further validation and broader recognition
SGA 80 patients with liver cirrhosis 97.3%(90.7, 99.7) 74.4 %(57.9, 87.0) 0.95 [31]
LDUST RFH-NPT 207 patients with liver cirrhosis 1. Food intake2. Weight loss3. Fat and muscle depletion4. Fluid accumulation5. Functional capacity 92.1% 67.2% 0.797 [32] Includes a fluid retention item, is rapid and self-administered, and has high sensitivity Relies entirely on patients’ subjective judgment and has a low negative predictive value

Phenotypic criteria: involuntary weight loss, BMI, and reduced muscle mass; Etiologic criteria: reduced food intake or impaired assimilation, as well as disease burden or inflammatory conditions. AIWW: Age, intake, weight loss, walk for physical activity; AUC: area under the curve; BMI: body mass index; GLIM: Global Leadership Initiative on Malnutrition; LDUST: Liver disease undernutrition screening tool; MNA-SF: Mini Nutritional Assessment-Short Form; MUST: Malnutrition Universal Screening Tool; NRS-2002: Nutritional Risk Screening 2002; PG-SGA: Patient-Generated Subjective Global Assessment; Phenotypic criteria: involuntary weight loss, body mass index, and reduced muscle mass; Etiologic criteria: reduced food intake or impaired assimilation, as well as disease burden or inflammatory conditions; RFH-NPT: Royal Free Hospital-Nutrition Prioritising Tool; SGA: Subjective Global Assessment; sPG-SGA: Scored Patient-Generated Subjective Global Assessment.

 MULTIDIMENSIONAL MECHANISMS UNDERLYING MALNUTRITION ASSOCIATED WITH HCC Gut-liver-muscle axis

Obesity, IR, MASLD, and sarcopenia form a dynamic network that promotes the progression from metabolic dysfunction to malignant transformation through the gut-liver-muscle axis. In the early stage of disease, visceral fat accumulation drives IR via the release of free fatty acids (FFAs). IR, on the one hand, enhances de novo lipogenesis (DNL) in the liver by upregulating sterol regulatory element-binding protein 1c (SREBP-1c); on the other hand, it accelerates muscle degradation by inhibiting the Akt/mTOR signaling pathway and upregulating myostatin, thereby activating the ubiquitin-proteasome system[33].

Notably, as the largest peripheral glucose reservoir, the progressive loss of skeletal muscle further undermines systemic insulin sensitivity, establishing a self-reinforcing loop of “IR-sarcopenia-MASLD”. Large-scale cohort studies have confirmed that this association is independent of obesity[34,35]. Within this pathological cascade, gut microbiota dysbiosis acts as a central hub linking MASLD to HCC-associated malnutrition. Clinical evidence indicates that HCC patients display distinct microbial imbalances superimposed on a MASLD background: a pronounced depletion of beneficial butyrate-producing taxa such as Faecalibacterium, alongside enrichment of potentially pathogenic genera including Streptococcus and Veillonella. In addition, small intestinal bacterial overgrowth (SIBO) is highly prevalent in MASLD populations, reaching up to 35%, and is frequently accompanied by diarrhea and vitamin B12 deficiency[36]. This dysbiosis disrupts intestinal barrier function through multiple molecular mechanisms: tumor necrosis factor-alpha (TNF-α) promotes the internalization of occludin, thereby impairing tight junctions; interleukin-13 (IL-13) upregulates claudin-2, creating a “pore pathway”; and zonulin, a key permeability regulator, further aggravates “leaky gut” via the PAR2-EGFR axis[37,38]. Compromised barrier integrity facilitates the translocation of lipopolysaccharide (LPS) and viable bacteria to the liver[39], where they activate innate immunity through the Toll-Like receptor 4 (TLR4)/nuclear factor-kappa B (NF-κB)/c-Jun N-terminal kinase (JNK) signaling pathway[40], thereby accelerating the progression of MASLD to HCC[41,42]. Critically, microbiota-immune-muscle crosstalk shapes the phenotype of cachexia. The chronic inflammatory milieu induced by LPS translocation directly drives muscle wasting: circulating levels of TNF-α and IL-6 are markedly elevated in MASLD-HCC patients with sarcopenia[43-45]. Mechanistically, TNF-α promotes muscle atrophy by accelerating ubiquitin-proteasome-mediated protein degradation, whereas inhibition of the IL-6/STAT3 pathway has been shown to enhance muscle repair in HCC mouse models[46-50]. Meanwhile, microbial metabolic reprogramming is pivotal. Short-chain fatty acid (SCFA) deficiency not only impairs GPR41/43-mTOR-mediated anabolic signaling in muscle but also suppresses protein translation by competitively consuming branched-chain amino acids (BCAAs)[51,52]. Moreover, reduced SCFAs are accompanied by lower glucagon-like peptide-1 (GLP-1) levels, further worsening IR and forming a vicious cycle of “metabolic dysfunction-sarcopenia”[53]. Genetic factors also modulate this network; for example, the patatin-like phospholipase domain-containing protein 3 (PNPLA3) rs738409 G allele exacerbates liver injury in the setting of low muscle mass[54].

In summary, the gut microbiota forms the core pathological network of MASLD-HCC-associated malnutrition by mediating barrier disruption, inflammatory cascades, and metabolite imbalances. Microbiota- or metabolite-targeted intervention strategies hold significant translational potential[55].

 Metabolic reprogramming

The hypermetabolism of tumor cells is primarily manifested by the Warburg effect and the Cori cycle. The Warburg effect describes the preference of tumor cells for glycolysis to generate ATP, even under aerobic conditions, leading to the release of large amounts of lactate[56]. Excess lactate enters the Cori cycle and is converted back into glucose via hepatic gluconeogenesis for systemic use[57]. This cyclical process results in inefficient energy expenditure[58], further increasing resting energy expenditure. Studies have shown that lactate, as a critical signaling molecule, can directly activate proopiomelanocortin (POMC) neurons and suppress appetite through astrocyte-neuron communication[59]. In addition, Liu et al. demonstrated that lactate induces browning of white adipose tissue via the adipocyte-specific G protein-coupled receptor (GPR81)[60]. As an intermediate product of metabolic reprogramming, lactate further aggravates malnutrition itself.

 Anorexia

Appetite is primarily regulated by two key hypothalamic neuronal populations: agouti-related peptide (AgRP) and POMC neurons[61,62]. Evidence indicates that pro-inflammatory cytokines (IL-1β, IL-6, TNF-α)[63-67], as well as lipocalin 2 (LCN2)[68], prostaglandin E2 (PGE2)[69], and insulin-like 3 (INSL3) produced within the tumor microenvironment, together with leptin and Metrnl secreted by adipose tissue[70], can modulate feeding behavior by acting on the hypothalamus. Borner et al. identified that GLP-1 signaling in the nucleus tractus solitarius is a critical mediator of anorexia in rats with HCC[71]. Further studies have shown that silencing the calcitonin gene-related peptide-parabrachial nucleus (CGRP-PBN) pathway in the nucleus tractus solitarius can reverse anorexia[66], suggesting that the nucleus tractus solitarius also serves as a key hub for appetite regulation.

Gut microbiota can influence appetite by modulating secondary bile acids through the farnesoid X receptor and Takeda G protein-coupled receptor 5 (TGR5), thereby inhibiting ghrelin secretion and stimulating neuropeptide Y (NPY)/AgRP neurons via the phospholipase C-Inositol 1,4,5-Trisphosphate (PLC-IP3)/diacylglycerol-protein kinase C (DAG-PKC) pathway[72]. In turn, SCFA regulate the secretion of peptide YY (PYY) and GLP-1[73], which can bind to GLP-1 receptors in the hypothalamus[74]. This suggests that gut microbiota dysbiosis may simultaneously act on central appetite-regulating centers via vagal stimulation and endocrine pathways, leading to decreased appetite[75-77]. Additionally, reduced appetite is associated with treatment-related side effects, such as taste disturbances[78,79] and diarrhea[80].

 NUTRITIONAL INTERVENTION

Oral nutrition, enteral nutrition, and parenteral nutrition form a three-tiered nutritional support strategy commonly used in clinical practice. MyPath, a patient-centered assessment and management system developed in the European Union, provides tailored, guideline-based care options[81]. This program is currently undergoing pilot testing and may, in the future, offer novel insights and experience to address the problem of inconsistent implementation of nutritional guidelines in clinical settings. In addition, the gut microbiota has demonstrated therapeutic potential in the management of various diseases, and we specifically focus on its current application and efficacy in HCC.

Fecal microbiota transplantation (FMT)

FMT is considered to have the potential to reshape the gut microbiota, restore microbial function, mitigate systemic inflammation, and reprogram the tumor microenvironment[82-84]. For instance, early-phase clinical trials in patients with refractory melanoma have demonstrated preliminary efficacy and acceptable safety[85], providing initial proof-of-concept for its use in oncology. However, clinical translation faces multiple barriers. First, donor screening is a core prerequisite to ensure safety and efficacy, yet the standards are extremely stringent. Current screening mainly follows modified blood-donor guidelines, using questionnaires and laboratory tests to exclude individuals at risk of infectious diseases, metabolic disorders, malignancies, and other conditions[86]. As a result, the proportion of eligible donors is very low. For example, in a Korean screening program, only 5% of volunteers met the criteria for repeat donation[87]. Similarly, under European standardized protocols, the eligibility rate is approximately 10%, and regular rescreening is required[88]. This substantially limits the scale and accessibility of donor banks while increasing treatment costs. Second, safety data in the specific population of cancer patients remain particularly scarce. Although approaches such as washed microbiota transplantation[89] and encapsulated formulations[90] aim to reduce risks, adverse events such as fever and vomiting may still occur after FMT[91]. Most importantly, for the endpoint of improving cancer-associated malnutrition, there is almost a complete lack of evidence from large-scale randomized controlled trials[92]. The efficacy of FMT may depend heavily on donor-recipient compatibility and complex microbial network effects[93], underscoring the need to develop personalized donor-matching strategies.

 Probiotics and prebiotics

The combined use of probiotics and prebiotics, known as synbiotics, has been redefined as a mixture of live microorganisms and selectively utilized substrates by host microorganisms, intended to confer health benefits[94]. The mechanisms by which synbiotics improve malnutrition are relatively well established: specific strains (e.g., Lactobacillus, Streptococcus faecalis) can reduce circulating LPS levels[95], thereby attenuating a key pathway linking gut dysbiosis to systemic inflammation. In MASLD models, probiotics such as Lactobacillus have been shown to improve IR, reduce inflammation, and restore the abundance of beneficial bacteria[96-98].

Moreover, Lactobacillus rhamnosus GG and Escherichia coli Nissle 1917 have been shown in models to enhance intestinal barrier integrity, increase protective mucus secretion, and improve SCFA metabolism[99,100]. However, orally administered probiotics face challenges such as degradation by gastric acid and bile salts, leading to reduced viability and colonization capacity[101,102], which directly affects the stability and reproducibility of their therapeutic effects. Pre-encapsulation strategies for Lactobacillus species, such as alginate hydrogel beads, Cas, and Alg, have been demonstrated to increase bacterial survival rates, as shown in Table 2.

Results of experiments with different encapsulation materials

Probiotics Material Results Ref.
Lactis Bb12 Poly(vinyl alcohol) An increase to 1.71 × 108 was observed after 1 week [103]
L. plantarum 550 Alginate hydrogel beads After 6 h of digestion, the loss of viable cells was only 1.09 log CFU/g, indicating a substantial improvement [104]
Lactobacillus acidophilus 11073 WPI-OSA-starch complex A significant (P < 0.05) survival rate (95.94% ± 1.64%) was observed after spray drying [105]
L. plantarum NCIMB 8826 Chitosan gel particles No significant decrease in cell concentration was observed during a 2-h incubation in simulated gastric fluid at pH 2 (P > 0.05) [106]
L. acidophilus CGMCC1.2686 Alginate and colloidal particles (TPC-stabilized nanoemulsion, NE)/TPC ALG + NE exhibited the best protective effect (8.93 ± 0.06 log CFU/g) [107]
L. plantarum FZU3013 Chitosan (Cas) and alginate (Alg) Compared with alginate beads, it better preserves bacterial structural integrity [108]
Lactobacillus plantarum ALG-WPI and ALG-PEC-WPI The survival rates increased by 65.37% and 72.06%, respectively [109]

Alg: alginate; ALG-PEC-WPI: alginate-pectin-whey protein isolate; ALG-WPI: alginate-whey protein isolate; Cas: caseinate; NE: nanoemulsion; OSA-starch: octenyl succinic anhydride starch; TPC: tea polysaccharide conjugates; WPI: whey protein isolate.

 CONCLUSION

This review systematically examines the distinctive clinical challenges posed by malnutrition in patients with MASLD-HCC. The central finding is that conventional etiology-based nutritional assessment tools - whether general or liver-specific - are increasingly insufficient to accurately capture the dynamically evolving nutritional risk in MASLD-HCC, shaped by the “gut-liver-muscle axis”. This pathogenic network includes intricate interactions among gut dysbiosis, metabolic derangements, systemic inflammation, and anorexia-related behaviors, presenting both a barrier to intervention and a critical target for therapeutic breakthroughs. Although microbiome-targeted approaches, such as FMT and probiotics, provide promising routes for addressing the underlying etiology, they remain largely exploratory, and current evidence regarding clinical efficacy and safety is still inadequate. Accordingly, future research should prioritize two key areas: first, the development of novel assessment tools that integrate dynamic measures of metabolism, inflammation, and body composition to enable early and precise identification of nutritional risk; second, the design and conduct of large-scale prospective intervention studies with clinically meaningful hard endpoints - such as muscle mass and survival - to clarify the role of microbiome modulation within comprehensive treatment strategies supported by robust evidence, ultimately improving outcomes for this expanding patient population.

 DECLARATIONS Authors’ contributions

Made contributions to the conception of the study: Huang BZ

Collected and analyzed the literature: Huang BZ

Contributed to manuscript discussion: Tian CB

Revised the manuscript for final submission: Huang BZ

Writing - review & editing: Sui YT

 Availability of data and materials

Not applicable.

 AI and AI-assisted tools statement

During the preparation of this manuscript, the AI tool Tencent Yuanbao (version 2.72.0, released 2026-06-12) was used solely for language editing. The tool did not influence the study design, data collection, analysis, interpretation, or the scientific content of the work. All authors take full responsibility for the accuracy, integrity, and final content of the manuscript.

 Financial support and sponsorship

This work was supported by the Shenzhen Basic Research Special Project (Grant No. JCYJ20240813145222029).

 Conflicts of interest

All authors declared that there are no conflicts of interest.

 Ethical approval and consent to participate

Not applicable.

 Consent for publication

Not applicable.

 Copyright

© The Author(s) 2026.

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