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Neuromuscular diseases are biologically diverse, clinically heterogeneous, and often difficult to diagnose and treat, highlighting the need for computational tools that can help resolve overlapping phenotypes and support timely, mechanism-informed interventions. This narrative review synthesizes recent advances in artificial intelligence (AI) and machine learning applied to neuromuscular diseases across diagnosis, outcome modeling, biomarker development, and therapeutics. AI-based approaches may assist clinical and genetic diagnosis from phenotypic data; however, early phenotype-driven tools have seen limited clinician adoption due to modest accuracy, usability challenges, and poor workflow integration. Electrophysiological studies remain central to diagnosing neuromuscular diseases, and AI shows promise for accurate classification of electrophysiological signals. Predictive models for disease outcome and progression—particularly in amyotrophic lateral sclerosis—are under active investigation, but most remain at an early stage of development and are not yet ready for routine clinical use. Digital biomarkers derived from imaging, gait, voice, and wearable sensors are emerging, with MRI-based quantification of muscle fat replacement representing the most mature and widely accepted application to date. Efforts to apply AI to therapeutic discovery, including drug repurposing and optimization of gene-based therapies, are ongoing but have thus far yielded limited clinical translation. Persistent barriers to broader adoption include disease rarity, data scarcity, heterogeneous acquisition protocols, inconsistent terminology, limited external validation, insufficient model explainability, and lack of seamless integration into clinical workflows. Addressing these challenges is essential to moving AI tools from the laboratory into clinical practice. We conclude with a practical checklist of considerations intended to guide the development and adoption of AI tools in neuromuscular disease care. Full article

B7-H3 (CD276), a member of the B7 family of proteins, is known to play a key role in the progression of a number of cancers. This protein is selectively expressed in both tumor cells and immune cells within the tumor microenvironment. Various investigations, including a number of clinical trials, have reported high levels of expression of this protein in cancerous tissues compared to their healthy counterparts. This difference in expression attracted various research efforts to establish whether such a difference can be linked to the therapeutic potential of this molecule. It is worth noting that B7-H3 is not the only immune checkpoint expressed at different levels in cancerous and healthy cells. Therapeutic strategies, based on different levels of expression, have been tested with other checkpoints. To inhibit the expression of some checkpoints, immune checkpoint inhibitors (ICIs) were developed. The introduction of these inhibitors for the treatment of some forms of advanced-stage tumors has been justly described as an important milestone in the landscape of immune therapy. Years after the launch of these inhibitors, numerous clinical trials revealed that these inhibitors benefit a narrow subset of patients suffering from advanced-stage tumors, while the majority of patients treated with these inhibitors either did not respond positively or simply did not respond at all (refractory patients). Other clinical trials showed that this form of treatment can provoke serious immune-related adverse events (irAEs). It is fair to state that changes in the expression level of a given protein in diseased tissue is an important parameter to take into account in the assessment of such a protein as a therapeutic target. However, the last ten years have demonstrated that taking the level of expression of a given checkpoint within a cancerous tissue is not sufficient to consider such expression a reliable predictive biomarker for the investigated disease. On the other hand, to establish a solid base for a given therapeutic strategy, these varying levels of expression have to be combined with a deep understanding of the biology of the molecule under investigation, as well as the identification and thorough analysis of the relevant signaling pathways, particularly those communicating with both the investigated molecule and the immune system. Recently, a number of pharmaceutical and biotechnology firms have suggested that B7-H3 is a highly promising therapeutic target for the development of immune therapeutics. In this review, we ask why hopes of better therapeutic performance are attached to this immune checkpoint. A partial answer to this question is provided through the careful consideration of the available data generated by various clinical trials. The contribution of mass spectrometry-based proteomics to this area of research is highlighted. Full article

Background: Thymic epithelial tumors (TETs), including thymic carcinomas and thymomas, are rare malignancies originating in the mediastinum. Therapeutic options remain limited for patients experiencing disease progression following platinum-based chemotherapy. High tumor mutational burden (TMB) is uncommon in thymic malignancies but may predict response to immunotherapy. We report a patient with TMB-high TET who participated in the KOSMOS-II study in South Korea and achieved a durable response to atezolizumab without developing immune-related adverse events (irAEs). Case presentation: A 73-year-old woman who had been treated for thymoma 20 years ago presented with a left neck mass. A biopsy of the neck mass confirmed recurrent thymoma, type B3, and her disease progressed despite platinum-based chemotherapy and subsequent pemetrexed treatment. TMB-high thymoma is very rare, but based on the next-generation sequencing (NGS) results, she was diagnosed with TMB-high (20.3 mutations/Mb) thymoma. As TMB-based immunotherapy is not approved in Korea, she was enrolled in the KOSMOS-II study and initiated on atezolizumab following molecular tumor board review. She achieved stable disease after three cycles and has remained progression-free for 14 months, completing 20 cycles without significant irAEs. Notably, her underlying myasthenia gravis did not worsen during treatment. Conclusions: This case demonstrates a favorable outcome with biomarker-directed ICI treatment in recurrent thymoma with limited treatment options, highlighting the importance of appropriate molecular markers to predict drug response. Although TMB-based immunotherapy is FDA-approved in the U.S., it remains unavailable in Korea, underscoring the need to explore flexible access pathways, including the potential use of immunotherapy beyond current indications, to improve treatment options for patients with life-threatening conditions. Full article

The 25 kDa glycoprotein lipocalin-2 (LCN2) is widely expressed and has diverse functions, ranging from physiological to pathophysiological processes. In the liver, LCN2 is primarily associated with inflammatory processes and is considered a potential biomarker in metabolic disorders. However, a significant challenge is the absence of a suitable human in vitro model for studying LCN2 and its associated signaling pathways. Therefore, we have successfully generated patient-derived liver organoids of both male and female origin, providing a novel in vitro model for LCN2 research. Our data show that the self-renewing organoids mimic essential architectural features of hepatocytes, as demonstrated by electron microscopy and F-actin staining. Consistent with the expression profile observed in liver tissue, the isolated 3D organoids exhibit minimal endogenous LCN2 levels. Next, the LCN2 expression was studied at the protein and mRNA levels under inflammatory conditions by treating the organoids with various cytokines and lipopolysaccharides (LPS). Our results show that LCN2 expression is significantly upregulated by IL-1β and TNF-α in an NF-κB-dependent manner, but remains unchanged with IL-6 or LPS. In conclusion, we have established human patient-derived liver organoids as a valuable model for investigating LCN2 signaling mechanisms. This study lays the foundation for future research on the role of LCN2 in liver pathologies, aiding in disease progression understanding and facilitating patient-specific treatment predictions. Full article

Background: People with Parkinson’s disease suffer from gait impairments. Clinical scales provide a limited and rater-dependent assessment of gait. Wearable sensors allow an objective characterization by capturing rhythm, pace, and signature patterns. This study investigated if sensor-derived gait parameters have prognostic value for short-term progression of gait impairments. Methods: A total of 111 longitudinal visit pairs were analyzed, where participants underwent clinical evaluation and a 4 × 10 m walking test instrumented with wearable sensors. Changes in the UPDRSIII gait score between baseline and follow-up were used to classify participants as Improvers, Stables, or Deteriorators. Baseline group differences were assessed statistically. Machine-learning classifiers were trained to predict group membership using clinical variables alone, sensor-derived gait features alone, or a combination of both. Results: Significant between-group differences emerged. In participants with UPDRSIII gait score = 1, Improvers showed higher median gait velocity ($0.81 \mathrm{m}/\mathrm{s}$) and stride length ($0.80 \mathrm{m}$) than Stables ($0.68 \mathrm{m}/\mathrm{s}$; $0.70 \mathrm{m}$) and Deteriorators ($0.59 \mathrm{m}/\mathrm{s}$; $0.68 \mathrm{m}$), along with lower stance time variability (3.10% vs. 4.49% and 3.75%; all $p<0.05$). The combined sensor-based and clinical model showed the best performance (AUC $0.82$). Conclusions: Integrating sensor-derived gait parameters with clinical score can support the identification of patients at risk of gait deterioration in the near future. Full article

The downregulation of Klotho in renal injury predicts the progression of chronic kidney disease (CKD). Klotho acts as an antagonist of the Wnt/β-catenin pathway, which is involved in the pathogenesis of proteinuria, glomerulosclerosis and tubulointerstitial fibrosis. We investigated whether losartan (L, angiotensin II type-1 receptor blocker) alone or combined with synthetic (tempol, T) or natural antioxidants (olive leaf extract, O) could alter Klotho/Wnt4/β-catenin signaling, thus reducing fibrosis and slowing the progression of focal segmental glomerulosclerosis (FSGS) in spontaneously hypertensive rats (SHR). The rats were divided into five groups. The control rats received a vehicle. The other groups received adriamycin (2 mg/kg, i.v., twice in a 3-week interval) for FSGS induction. Treatments with L, L+T and L+O (10, 10 + 100 and 10 + 80 mg/kg/day, respectively) were administered by gavage during six weeks. In the kidneys of model rats, Klotho and Wnt4 were downregulated, whereas β-catenin and fibronectin levels were increased compared with the control group. L+T did not alter Klotho, Wnt4 or fibronectin levels, while it further increased β-catenin. In contrast, L+O improved Klotho, and reduced β-catenin and fibronectin levels, although it increased PAI-1. The L+O combination reduced proteinuria more efficiently than L and decreased renal injury close to control levels. Although these findings indicate that combined treatment of losartan and olive leaf extract is promising in slowing the progression of the experimental FSGS, further clinical studies are needed to confirm its favorable outcomes and safety in CKD patients. Full article

Coronary artery disease (CAD) remains the leading cause of cardiovascular morbidity and mortality worldwide, with plaque composition and morphology being as key determinants of disease progression and clinical outcomes. Accurate plaque characterization is essential for risk stratification and therapeutic decision-making, yet conventional image interpretation is limited by inter-observer variability and time-intensive workflows. Artificial intelligence (AI) models have emerged as a transformative tool for automated coronary plaque analysis across multiple imaging modalities. AI-driven models demonstrate high diagnostic accuracy for plaque detection, segmentation, quantification, and vulnerability assessment. Integration of AI-derived imaging biomarkers with clinical risk scores can further enhance prediction of major adverse cardiovascular events and supports personalized management. These advances position AI-enhanced imaging as a powerful adjunct for both invasive and non-invasive evaluation of CAD. Despite its promise, important barriers to widespread clinical adoption remain, including data heterogeneity, algorithmic bias, limited model transparency, insufficient prospective validation, regulatory challenges, and incomplete integration into clinical workflows. Addressing these challenges will be essential to ensure safe, generalizable, and cost-effective implementation of AI in routine cardiovascular care. Full article

Triple-negative breast cancer (TNBC), which is characterized by a lack of the estrogen receptor, the progesterone receptor, and HER2 expression, is the most aggressive breast cancer subtype and has a poor prognosis and high recurrence rates because of frequent chemotherapy resistance. As a crucial epigenetic regulator, DNA methylation modulates gene expression through aberrant methylation patterns, contributing to tumor progression and therapeutic resistance. Early diagnosis and treatment of TNBC are vital for its prognosis. The development of DNA methylation testing technology and the application of liquid biopsy provide technological support for early diagnosis and treatment. Additionally, preclinical and early-phase clinical studies suggest that epigenetic therapies targeting DNA methylation may hold promise for TNBC treatment, pending larger clinical trials. Furthermore, research on DNA methylation-based prognostic models enables personalized precision treatment for patients, helping to reduce unnecessary therapies and improve overall survival. The emerging role of DNA methylation patterns in predicting the therapeutic response and overcoming drug resistance is highlighted. In this narrative review, we integrate current research findings and clinical perspectives. We propose that DNA methylation presents promising research prospects for the diagnosis, treatment and prognosis prediction of TNBC. Future efforts should focus on translating methylation-driven insights into clinically actionable strategies, ultimately advancing precision oncology for this challenging disease. Full article

Background/Objectives: Atypical femoral fracture (AFF) represents a diagnostic and therapeutic challenge, particularly in autoimmune disease patients receiving long-term bisphosphonate (BP) and glucocorticoid (GC) therapy. Although bilateral AFF is common, the radiographic evolution of asymptomatic incomplete lesions identified at the time of a complete fracture remains insufficiently defined. This study aimed to characterize the natural history and imaging biomarkers associated with progression in this biologically homogeneous high-risk population. Methods: Ten female autoimmune disease patients with complete AFF and asymptomatic incomplete contralateral lesions were retrospectively evaluated over a mean 59 months. Serial radiographs were assessed for cortical beaking, periosteal flaring, and transverse radiolucent lines. All patients discontinued BP therapy postoperatively; teriparatide was administered when tolerated. Results: Six lesions regressed, three remained stable, and one progressed—this progressing case being the only limb with a transverse radiolucent line at baseline. No patient developed symptoms or sustained a complete fracture on the contralateral side. Radiographic remodeling occurred independently of symptoms. BP discontinuation and, when tolerated, teriparatide appeared to contribute to lesion stabilization, although statistical significance was not achieved. Conclusions: In autoimmune patients with severe long-term BP and GC exposure, most asymptomatic incomplete AFF identified at the time of contralateral complete fracture remains stable or improves under conservative management. A transverse radiolucent line is the most decisive imaging biomarker predictive of progression and warrants intensified surveillance or consideration of prophylactic fixation. Larger cohorts are needed to refine risk stratification algorithms and optimize diagnostic and management strategies. Full article

Introduction: The global prevalence of chronic kidney disease (CKD) is increasing and it is associated with higher mortality rates. Transforming growth factor-beta (TGF-β) can serve as a novel biomarker for early prediction of chronic kidney disease (CKD) progression. Methods: This was a prospective longitudinal study among black patients with CKD who attended the Charlotte Maxeke Johannesburg Academic Hospital between September 2019 and March 2022. Patients provided urine and blood samples for laboratory investigations at study entry (0) and at 24 months follow up. Baseline serum and urine TGF-β1, TGF-β2 and TGF-β3 levels were measured using ELISAs. Multivariable logistic regression analysis was utilized to determine if TGF-β isoforms could predict CKD progression. Results: A total of 312 patients were enrolled at baseline, of whom 275 (88.1%) had early-stage CKD (Stage 1–3). A majority, 95.2% (297/312), of the patients completed the study after 2 years follow up. The prevalence of CKD progression was 47.8% when measured by a sustained decline in eGFR of >4 mL/min/1.73 m²/year or more and 51.9% when measured by a change in uPCR > 30%. The patients with CKD progression had significantly lower eGFR and increased urine protein–creatinine ratios compared to non-progressors. Furthermore, comparing progressors with non-progressors, the median serum TGF-β1 was 21210 (15915–25745) ng/L vs. 24200 (17570–29560) ng/L and the median urine TGF-β3 was 17.5 (5.4–76.2) ng/L vs. 2.8 (1.8–15.3) ng/L, respectively. Baseline serum and urine TGF-β isoforms were unable to discriminate between CKD progressors and non-progressors after multivariable logistic regression analysis. Conclusions: Despite the multiple roles of TGF-β isoforms in kidney disease, baseline levels were not predictive of chronic kidney disease progression. Full article

Ovarian cancer remains the most lethal gynaecological malignancy primarily due to late-stage diagnosis, high recurrence rate, and limited treatment efficacy. Current diagnostic tools, including imaging and serum markers, lack sufficient sensitivity and specificity for early detection. Increasing evidence highlights the critical role of myeloid-derived immune cells within the tumour microenvironment in shaping ovarian cancer progression and therapy response. Monocytes and their derivatives are central regulators of immune suppression, chemoresistance, and metastatic dissemination in ovarian tumours. Their recruitment and polarisation are governed by several signalling pathways offering promising therapeutic targets. Strategies including monocyte depletion, TAM reprogramming, MDSC maturation, DC vaccines, and their synergistic use with chemotherapy or immune checkpoint inhibitors are being explored to restore anti-tumour immunity in ovarian cancer. Parallel to therapeutic potential, the lymphocyte-to-monocyte ratio and its reciprocal monocyte-to-lymphocyte ratio have also emerged as potential accessible and cost-effective prognostic tools that predict disease aggressiveness and survival in ovarian cancer. This review features the diagnostic, prognostic, and therapeutic significance of monocytes and their derivatives in ovarian cancer management and highlighting new opportunities for next-generation immunomodulatory therapies. Full article

Background/Objectives: Atezolizumab plus bevacizumab (Atz+Bev) is widely used for advanced hepatocellular carcinoma (HCC), yet predictors of post-progression survival (PPS), a clinically meaningful endpoint reflecting the feasibility of treatment sequencing, remain unclear. We aimed to identify determinants of PPS and factors associated with successful transition to subsequent therapy after progressive disease (PD) on Atz+Bev. Methods: We retrospectively analyzed 132 patients with HCC who initiated Atz+Bev with Child–Pugh A and Eastern Cooperative Oncology Group performance status (ECOG PS) 0/1. PPS was defined as survival from radiological PD to death; tumor response was assessed by RECIST v1.1. Results: Among 132 patients treated with Atz+Bev, median progression-free and overall survival were 9.2 and 21.2 months. PD occurred in 97 patients, with a median PPS of 9.2 months. At PD, 76 patients (78.4%) maintained both Child–Pugh A and ECOG PS 0/1; 93.4% of these patients transitioned to subsequent therapy, compared with 38.0% of patients who did not maintain Child–Pugh A and ECOG PS 0/1. The median PPS values were 14.7 and 2.0 months, respectively (p < 0.0001). In this PD cohort, disease control achieved with subsequent therapy after radiological PD was associated with longer PPS (16.1 vs. 5.0 mosnths; p = 0.0002). ECOG PS 0, Child–Pugh A, absence of portal vein invasion, and AFP < 400 ng/mL at PD independently predicted prolonged PPS. A baseline Child–Pugh score of 5 independently predicted preservation of Child–Pugh A and ECOG PS 0/1 at PD. Conclusions: Initiating Atz+Bev under optimal liver function (Child–Pugh 5) and preserving hepatic reserve and performance status through progression are critical for enabling subsequent therapy and achieving longer PPS. Full article

Although the impact of COVID-19, caused by SARS-CoV-2, may appear to have diminished in recent years, the emergence of new variants still continues to cause significant global health and economic challenges. While numerous metabolomic studies have explored serum-based alterations linked to the infection, investigations utilizing urine as a biological matrix remain notably limited. This gap is especially significant given the potential advantages of urine, a non-invasive and easily obtainable biofluid, in clinical settings. In the context of patients in intensive care units (ICUs), temporal monitoring through such non-invasive samples may offer a practical and effective approach for tracking disease progression and tailoring therapeutic interventions. This study retrospectively explored the longitudinal metabolomic alterations in COVID-19 patients admitted to the ICU, stratified into three prognostic outcome groups: healthy discharged (HD), polyneuropathic syndrome (PS), and Exitus. A total of 32 urine samples, collected at four distinct time points per patient during April 2020 and preserved at −80 °C, were analyzed by proton nuclear magnetic resonance (¹H-NMR) spectroscopy for comprehensive metabolic profiling. Statistical evaluation using two-way ANOVA and ANOVA–Simultaneous Component Analysis (ASCA) identified significant prognostic variations (p < 0.05) in the levels of taurine, 3-hydroxyvaleric acid and formic acid. Complementary supervised classification via random forest modeling yielded moderate predictive performance with out-of-bag error rate of 40.6% based on prognostic categories. Particularly, taurine, 3-hydroxyvaleric acid and formic acid levels were highest in the PS group. However, no significant temporal changes were observed for any metabolite in analyses. Additionally, metabolic pathway analysis conducted using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database highlighted the “taurine and hypotaurine metabolism” pathway as the most significantly affected (p < 0.05) across prognostic classifications. Harnessing urinary metabolomics, as indicated in our preliminary study, could offer valuable insights into the dynamic metabolic responses of ICU patients, thereby facilitating more personalized and responsive critical care strategies in COVID-19 patients. Full article

Background/Objective: Early detection of metastatic progression remains a major challenge in precision oncology. Conventional radiological imaging cannot reliably identify micrometastatic disease. Although circulating tumor DNA is promising for minimal residual disease detection, organ-derived response biomarkers reflecting tissue adaptation to secreted factors remain unexplored. We hypothesized that integrating such biomarkers with global laboratory parameters would generate a synthetic variable with improved discrimination for de novo metastasis and mortality. Methods: This prospective observational pilot study enrolled 30 patients (median age 64.4 years; 56.7% female) with heterogeneous solid malignancies. Peripheral blood biomarkers responsive to tumor-secreted soluble factors (n = 11) were quantified using a multiplexed beads Luminex immunoassay. Global analytical parameters (n = 20) were derived from routine laboratory assessments. Hierarchical agglomerative clustering analysis generated two synthetic variables: Stigma (Ϛ) and Qoppa (Ϙ). Receiver operating characteristic curve analysis, Kaplan–Meier survival analysis, and Cox regression were used to evaluate the performance. Results: Qoppa demonstrated acceptable discriminatory performance for de novo metastasis (AUC = 0.78). For mortality prediction, performance varied by disease status (overall AUC = 0.78): superior in non-metastatic patients (AUC = 0.98) but negligible in those with baseline metastases. Kaplan–Meier analysis confirmed significant survival differences (p = 0.042 overall survival; p = 0.024 for metastasis-free survival in the non-metastatic subgroup). Differences in biomarker expression and clinical variables (stage, tumor burden, and metastatic burden) were observed between the high and low Qoppa strata. Conclusions: In this small heterogeneous pilot cohort, Qoppa provides a proof of concept that integrating organ-derived response biomarkers with routine laboratory parameters may capture clinically relevant signals for metastatic risk stratification in oncology patients. This composite parameter supports the generation of hypotheses for future biomarker-driven research and clinical test development. External validation in larger multicenter cohorts is required before clinical implementation. Full article

Background/Objectives: Chronic Kidney Disease (CKD) is a global public health burden with a rising prevalence driven by population aging. Existing prediction models, such as the Kidney Failure Risk Equation (KFRE), often lack generalizability across ethnicities and comprehensive systemic indicators. This study aimed to develop and validate a machine learning model for predicting CKD progression by integrating traditional risk factors with novel composite indicators reflecting systemic health. Methods: Data from the China Health and Retirement Longitudinal Study (CHARLS, n = 2500) was used for model training. External validation was performed using independent cohorts from the English Longitudinal Study of Ageing (ELSA, n = 1200) and the Health and Retirement Study (HRS, n = 1500). Multiple machine learning algorithms, including XGBoost, were employed. Feature engineering incorporated composite indicators such as the frailty index (FI), triglyceride–glucose (TyG) index, and aggregate index of systemic inflammation (AISI). Results: The XGBoost model achieved an area under the curve (AUC) of 0.892 in the training set and maintained robust performance in external validation (AUC 0.867 in ELSA, 0.871 in HRS), outperforming the KFRE (AUC 0.745). SHAP analysis identified the FI as the most influential predictor. Decision curve analysis confirmed the model’s clinical utility. Conclusions: This machine learning model demonstrates high accuracy and cross-ethnicity validity, offering a practical tool for early intervention and personalized CKD management. Future work should address limitations such as the retrospective design and expand validation to underrepresented regions. Full article