Biomedicines

Circulating stem and progenitor cells (SPCs), including mesenchymal stromal cells (MSCs) and hematopoietic stem/progenitor cells (HSPCs), are mobilised after tissue injury but their temporal behaviour after hemorrhagic shock (HS) and relationship to cytokine milieus and outcome remain unclear. In a prospective observational cohort at JPN Apex Trauma Centre, AIIMS, New Delhi we studied 100 participants: 50 trauma patients with hemorrhagic shock and traumatic brain injury (HS index group), 25 trauma patients without HS, and 25 minor-injury controls. Peripheral blood was collected at admission (day 0) for all groups and additionally at days 3, 7 and 14 for the HS group. PBMCs were phenotyped by flow cytometry (HSPC markers: CD45, CD123, CD38, CD34; MSC markers: CD105, CD73, CD90) and serum SDF-1, VEGF-A, EGF, GRO- and GRO-{beta}, GM-CSF and G-CSF were measured by ELISA; group and time effects were evaluated with mixed-effects models and correlations by Spearman tests (two-tailed p<0.05). At admission, trauma patients without HS had significantly higher MSC and HSPC-like populations versus controls (p<0.0001). In the HS cohort SPC percentages rose modestly at day 0-3 then declined sharply by days 7-14 (time effect p<0.0001); non-survivors exhibited significantly higher early SPC and cytokine levels that persisted until death while survivors showed an early rise followed by decline (outcome and time interaction p<0.0001). All cytokines were up-regulated in trauma groups, peaked at day 0-3 in HS patients, and correlated positively with SPC counts (notably SDF-1, VEGF-A, G-CSF, Gro- and GM-CSF; Spearman p<0.05); higher early SPC and cytokine signatures associated with greater organ dysfunction (higher SOFA) and with timing of sepsis. These findings indicate that trauma provokes an early SPC and cytokine response that in HS is followed by later decline, and that persistent early elevation predicts worse outcomes, suggesting serial SPC and cytokine profiling may have prognostic value and identify an early therapeutic window for regenerative or immunomodulatory interventions.

BackgroundDespite extensive characterization of key oncogenic drivers, pancreatic ductal adenocarcinoma (PDAC) continues to exhibit profound molecular heterogeneity and inconsistent responses to standard therapies, including gemcitabine. The role of pathway-level alterations, particularly in the context of age at onset and therapeutic exposure, remains insufficiently defined. MethodsIn this study, we leveraged a conversational artificial intelligence framework (AI-HOPE-TP53 and AI-HOPE-PI3K) to enable precision oncology, driven interrogation of clinical and genomic data from 184 PDAC tumors, stratified by age at diagnosis and gemcitabine exposure. Using AI-enabled cohort construction and pathway-centric analyses, we evaluated alterations in TP53 and PI3K signaling networks, with findings validated through conventional statistical methods. ResultsTP53 pathway analysis revealed a significantly higher frequency of TP53 mutations in early-onset compared to late-onset PDAC among gemcitabine-treated patients (86.7% vs. 57.1%, p = 0.04), with a similar trend observed between treated and untreated early-onset cases (86.7% vs. 40%, p = 0.07). Notably, in late-onset PDAC patients not treated with gemcitabine, absence of TP53 pathway alterations was associated with improved overall survival (p = 0.011). Complementary analyses of the PI3K pathway demonstrated a higher prevalence of pathway alterations in late-onset gemcitabine-treated tumors compared to untreated counterparts (13.2% vs. 2.7%, p = 0.02). Importantly, among late-onset patients not receiving gemcitabine, those without PI3K pathway alterations exhibited significantly improved overall survival (p < 0.0001). ConclusionTogether, these findings identify distinct TP53 and PI3K pathway dependencies that are modulated by both age-of-onset and treatment exposure in PDAC. This work highlights the utility of conversational artificial intelligence in enabling rapid, integrative, and hypothesis-generating analyses within a precision oncology framework, supporting the identification of clinically relevant molecular stratification strategies for this aggressive disease.

Hypermobile Ehlers-Danlos Syndrome (hEDS) is a genetic connective tissue disorder characterized by hypermobile joints, chronic pain, fatigue, brain fog, orthostatic intolerance, and GI symptoms and dysmotility. Its heterogeneous presentation contributes to poor quality of life, inappropriate interventions, and prolonged diagnostic delays, often up to 10 years. This study primarily aimed to determine if physiological signals captured by a medical-grade wrist wearable could characterize autonomic patterns in hEDS and relate them to symptoms. Individuals with hEDS (n=30) and healthy controls (n=28) wore a medical grade smartwatch for 30 days, collecting continuous heart rate variability, activity, oxygen saturation, and blood pressure, alongside initial baseline symptom and quality-of-life surveys. Individuals with hEDS showed greater instability and variability in both systolic and diastolic blood pressure as well as the HRV metric LF/HF ratio, in comparison to healthy controls (p-values: 0.04, 0.02, 0.02). During sleep, metrics of parasympathetic activity (HRV measures: HF power, pNN50, RMSSD) trended lower in hEDS than healthy in comparison. As expected, survey domains assessing physiologic symptoms and quality-of-life were significantly worse in the hEDS cohort (p-values < 0.05). Notably, autonomic metrics correlated with GI symptoms in the hEDS cohort (Spearman's {rho} range: 0.38-0.60), and psychological symptoms in the healthy cohort (Spearman's {rho} range: -0.47-0.41). Principal component analysis (PCA) of physiologic and symptom features clearly separated groups, supporting distinct physiologic profiles. Combination of GI symptom index and wearable monitoring show promise as a hybrid screening approach that could substantially shorten the time to diagnosis in this population.

Skeletal muscle regeneration is often impaired after acute muscle damage induced by viperid snake venoms, such as that of Bothrops asper, a medically-relevant species in Latin America. It has been shown that traces of venom that remain in the damaged muscle affect myogenic cells in culture, raising the possibility of inhibition of these toxins during the regenerative process as a way to improve regeneration. Using a mouse model of myonecrosis and regeneration, we evaluated the effects of Varespladib (a phospholipase A2 inhibitor) or Marimastat (a metalloproteinase inhibitor) on muscle regeneration when administered intravenously 24 h after the onset of myonecrosis, i.e., after muscle damage has occurred. The regenerative process was evaluated 14 and 28 days after venom injection. Results show that Marimastat, or a combination of both inhibitors, improved the extent of skeletal muscle regeneration and reduced the extent of tissue fibrosis when compared to tissue from mice receiving venom and no inhibitors, as judged by qualitative and quantitative histological assessment. Results underscore the deleterious role of traces of venom components in the damaged muscle during muscle regeneration and suggest that the administration of metalloproteinase inhibitors, or a combination of metalloproteinase and phospholipase A2 inhibitors, even when muscle damage has developed, may be a therapeutic alternative for improving the extent of muscle regeneration.

Background: Mucopolysaccharidosis type IIIA (MPS IIIA; Sanfilippo syndrome) is a fatal neurodegenerative lysosomal storage disorder caused by impaired degradation of heparan sulfate (HS). Despite rapid advances in gene and enzyme therapies, there remains a critical need for an analytically validated, quantitative biomarker that accurately reflects central nervous system (CNS) substrate burden. Such biomarker would be a valuable tool in assessing disease progression and monitoring therapeutic efficacy. Objective: This study describes the method development, fit for purpose validation, and preliminary clinical application of a quantitative liquid chromatography-mass spectrometry (LC-MS/MS) assay for the HS-derived disaccharide N-sulfoglucosamine-glucuronic acid (GlcNS-GlcUA) in human cerebrospinal fluid (CSF), a critical biomarker for diagnosis, disease monitoring, and regulatory evaluation of emerging MPS IIIA therapies. Methods: A structurally defined GlcNS-GlcUA reference standard and its [13C6]-labeled internal standard were used in a derivatization and detection workflow employing 1-phenyl-3-methyl-5-pyrazolone labeling, and LC-MS/MS. Results: The method exhibited acceptable linearity across 0.005-0.500 nmol/mL (r[&ge;]0.9976), with intra- and inter-assay imprecision [&le;]3.5%CV and accuracy within 95%-110% of nominal concentrations. No matrix or hemolysis interference or carryover was observed, and the analyte remained stable during freeze-thaw storage conditions. Application of the method to 12 CSF samples from patients with MPS IIIA demonstrated quantifiable GlcNS-GlcUA levels ranging from 0.0054 to 0.106 nmol/mL, confirming suitability for clinical and regulatory use. Comparison of the MPS IIIA sample results between the development laboratory and the contract research organization laboratory support robust inter-lab assay transfer. Conclusions: This validated LC-MS/MS method establishes a regulatory-grade quantitative assay for measurement of CSF HS in MPS IIIA. Its high analytical sensitivity and reproducibility enable reliable assessment of CNS substrate reduction and pharmacodynamic response, supporting biomarker-driven therapeutic development and accelerated approval pathways for neuronopathic mucopolysaccharidoses.

BackgroundThe X-linked muscle wasting disorder Duchenne muscular dystrophy (DMD) is a progressive and ultimately fatal disease caused by loss of function mutations in the dystrophin (DMD) gene. Upregulation of utrophin (UTRN), an embryonic homologue of dystrophin, has been proposed as a therapeutic option that could ameliorate disease. We previously generated a bioluminescent screen for utrophin-upregulating compounds using a mouse reporter of endogenous utrophin expression and discovered that inhibition of ERK1/2 and EZH2, increases utrophin expression in myoblasts. MethodologyHere we extend this analysis to show that treatment of human myoblasts with the ERK1/2 inhibitor LY3214996 and the EZH2 inhibitor GSK503, increases UTRN expression in primary and immortalised myoblasts derived from healthy volunteers and DMD patients. ResultsShort-term (24 hours) inhibition of ERK1/2 and EZH2 resulted in increased expression of utrophin in proliferating myoblasts. Surprisingly, in patient-derived samples, but not healthy controls, increased UTRN expression was sustained following drug removal and in vitro differentiation. Furthermore, dystrophin deficient myoblasts have altered expression of myogenic transcription factors MYOD1 and MYOG and proliferation marker Ki67, signalling an altered regenerative capacity of these cells, while ERK1/2 inhibition, alone or combined with EZH2i, reversed this transcriptional signature. ConclusionsTreatment with ERK1/2 and EZH2 inhibitors could offer a therapeutic option for DMD by increasing UTRN and MYOD1 expression. We propose that this may compensate for DMD loss and help restore productive muscle differentiation and regeneration.

Colorectal cancer (CRC) is a leading cause of cancer-related death, with incidence rising substantially among individuals under 50 years of age. Polygenic risk scores (PRS) hold promise for identifying high-risk individuals; when combined with lifestyle factors, they substantially improve prediction accuracy compared with models based on lifestyle factors alone. However, few clinical tools currently exist that facilitate this integrated, PRS-enhanced risk assessment. To bridge this gap, we developed MyGeneRisk Colon, a publicly accessible web portal that delivers individualized CRC risk prediction by incorporating genetic, demographic, family history, and lifestyle factors. This paper details the development of the underlying risk prediction model, the portal's architecture and data security, our reporting framework, and engagement with a community advisory panel. Designed as a user-friendly platform, MyGeneRisk Colon aims to effectively communicate personalized CRC risk profiles and educate users and healthcare providers about prevention strategies.

Sepsis can lead to acute respiratory distress syndrome (ARDS) and is associated with a high mortality rate. This study investigated cellular senescence-related genes in sepsis and sepsis-induced ARDS to identify novel biomarkers. Using bioinformatics analyses including WGCNA and machine learning on public datasets, six hub genes (NFIL3, GARS, PIGM, DHRS4L2, CLIP4, LY86) were identified. These genes showed strong diagnostic value and were associated with immune cell infiltration and key pathways. Validation in lipopolysaccharide (LPS)-stimulated neutrophils showed significant upregulation of NFIL3. The findings highlight the role of cellular senescence in pathogenesis and identify promising therapeutic targets for sepsis-induced ARDS.

Tyrosine Kinase inhibitors (TKIs) are widely used as effective chemotherapeutic agents for treating patients with EGFR-mutated NSCLC. Unfortunately, after treatment, patients eventually develop resistance to TKI therapy. The most common resistance mechanism for the TKI Osimertinib is the overexpression of the MET Proto-Oncogene, Receptor Tyrosine Kinase (MET). We previously demonstrated that miR-411-5p A-to-I edited at position 5 (miR-411ed) can directly target MET in A549 and H1299 cells. MiR-411ed in combination with Osimertinib reduced cell proliferation in two TKI resistant EGFR-mutated cell lines: HCC827R and PC9R. MiR-411ed did not downregulate MET expression in HCC827R, suggesting an alternative mechanism for TKI response. In this study, we aim to identify the mechanism of miR-411ed TKI response using a multi-omics approach of RNAseq and protein mass spectrometry. In our cellular model, we identified miR-411ed affected genes independent of MET activity, resulting in 211 genes (RNAseq) and 36 proteins (proteomics). Pathway analysis identified an increase in interferon signaling for RNAseq and combined omics, and a decrease in ERK/MAPK signaling in proteomics. Using the IsoTar target prediction tool, we identified STAT3 as a key regulator and confirmed STAT3 protein downregulation upon transfection with miR-411ed. We further investigated the effect of miR-411ed in vivo, observing a reduction in tumor size with miR-411ed in combination with Osimertinib but not with miR-411ed or Osimertinib treatment alone, confirming the effectiveness of miR-411ed in TKI response.

Background: Transthyretin cardiomyopathy (ATTR-CM) is a progressive, potentially fatal disease requiring accurate risk stratification. Echocardiography is the first-line imaging modality, with AI-based tools increasingly applied for automated analysis, yet their prognostic value remains unknown. Objectives: To examine the prognostic value of AI-derived echocardiographic measurements and their incremental value beyond biomarker staging in ATTR-CM. Methods: This retrospective study included patients from two ATTR-CM registries. Baseline echocardiograms were analyzed using the fully automated AI-based software Us2.ai. Prognostic performance was assessed by Kaplan-Meier analysis, Cox regression, and ROC curves. A two-parameter echocardiographic staging system combining left ventricular (LV) global longitudinal strain (GLS) and right ventricular (RV) fractional area change (FAC) stratified patients into low (both normal), intermediate (one abnormal), and high risk (both abnormal). Results: Among 347 patients (91% male, median age 78 years), 141 experienced all-cause death or heart failure hospitalization over a median follow-up of 2.4 years. In multivariable analysis, AI-derived LV-GLS (HR 1.13 [1.03-1.25], p=0.011) and RV FAC (HR 0.96 [0.93-0.99], p=0.014) were independent outcome predictors. Echo staging stratified risk into groups with 3-fold (95% CI 1.70-5.91) and 6-fold (95% CI 3.22-10.30) increased hazard compared to low risk (p<0.001), with incremental prognostic value beyond National Amyloidosis Centre (NAC) staging and age (chi-square from 53 to 80; p<0.001). AI and human measurements showed comparable 1-year predictive performance (all p>0.05). Conclusion: AI-derived echocardiographic measurements demonstrate independent and incremental prognostic value beyond biomarker-based NAC staging in ATTR-CM, comparable to human measurements, supporting their integration into clinical risk stratification.

Abstract Background: ST-elevation myocardial infarction (STEMI) is reported to be a leading cause of mortality worldwide. While cardiac troponins are the gold standard for myocardial injury detection but creatine kinase-MB (CK-MB) and total creatine phosphokinase (CPK) retain prognostic use in resource-limited settings. Objective: To evaluate the prognostic significance of admission CK-MB and CPK levels in STEMI patients and to assess their association with hematological parameters for integrated risk stratification. Methods: This cross-sectional study enrolled 15 consecutive STEMI patients from the Punjab Institute of Cardiology, Lahore, during January 2024. Comprehensive laboratory analysis including cardiac biomarkers (CK-MB, CPK, troponin-I, LDH), complete blood count, renal function, serum electrolytes, and metabolic parameters, was performed on admission. Pearson correlation and comparative statistical analyses were also conducted to assess the relationships between cardiac biomarkers and hematological indices. Results: The cohort includes 15 patients (mean age 50.1 +/- 12.2 years; 73.3% male). Cardiac biomarker elevation was prevalent: CK-MB was elevated in 12/15 (80%), CPK was elevated in 12/15 (80%), with concordant elevation in 11/15 (73.3%), which indicates extensive myocardial necrosis. Troponin-I showed the highest elevation rate at 13/15 (86.7%). Hematological abnormalities included anemia (60%), WBC elevation (53.3%), and RBC reduction (40%). Random glucose averaged 150.80 +/- 63.55 mg/dL, with 66.7% highlighted the hyperglycemia. Remarkably, electrolyte balance was preserved in all of the patients (0% sodium, potassium, and bicarbonate abnormalities), indicating maintained homeostasis. Pearson correlation analysis revealed a significant correlation between CK-MB and CPK (r = 0.615, p = 0.0126), while correlations between cardiac biomarkers and hematological parameters were weak (p > 0.05). Risk stratification identified 53.3% of patients as high-risk who required intensive management. Conclusions: CK-MB and CPK demonstrate significant concordance and retain prognostic value in STEMI patients, particularly in resource-limited settings where troponin access may be constrained. While troponin-I remains the most sensitive biomarker, combined assessment of conventional cardiac enzymes supports reliable evaluation of myocardial injury. Hematological parameters reflect systemic response but show limited correlation with cardiac biomarkers.

Background: CD276 has been proposed as a candidate gene associated with the biological characteristics of meningioma, but its predictive position and interpretive significance within a transcriptomic classifier have not yet been clearly established. Accordingly, this study aimed to evaluate CD276 stepwise across internal model development, external validation, calibration, decision-analytic assessment, feature stability, and robustness analyses using public transcriptomic cohorts. Methods: The analyses in this study were organized into two interconnected notebooks. In Notebook A, we reconstructed the internal training cohort (GSE183653), evaluated the CD276 single-gene signal, and then developed a transcriptome-wide multigene classifier. We also performed permutation importance, bootstrap confidence interval, label permutation test, repeated cross-validation, CD276 ablation, and internal calibration analyses. In Notebook B, we reproduced the external validation cohort (GSE136661) in a fixed common-gene space, applied train-only recalibration and train-only threshold transfer, and extended the interpretation through decision curve analysis, stability analysis, enrichment analysis, and one-factor-at-a-time robustness analysis. Results: The internal training cohort consisted of 185 samples and 58,830 genes, of which 25 were WHO grade III cases. CD276 expression showed a significant association with WHO grade, but the internal discrimination of the CD276-only baseline was limited (ROC-AUC 0.628, average precision 0.323, balanced accuracy 0.540). In contrast, the initial transcriptome-wide model showed ROC-AUC 0.834 and PR-AUC 0.509, and under 5-fold cross-validation, the canonical fulltranscriptome model and the CD276-forced 5,001-feature branch showed mean ROC-AUC/PR-AUC of 0.854/0.564 and 0.855/0.606, respectively, outperforming the CD276-only baseline at 0.644/0.391. CD276 was not included in the initial 5,000-feature filtered set and ranked 900th among 5,001 features even in the forcibly included 5,001-feature branch. In paired ablation analysis, the performance difference attributable to inclusion of CD276 was effectively close to zero (delta ROCAUC 0.000062, delta PR-AUC 0.000056). Internal calibration analysis showed an overconfident probability pattern (Brier score 0.10501, intercept -1.421392, slope 0.413241). In external validation, the fixed multigene pipeline achieved ROC-AUC 0.928 and PR-AUC 0.335. Train-only recalibration improved calibration metrics while preserving discrimination, and decision curve analysis showed threshold-dependent but limited external utility. Stability analysis showed overlap between core-stable genes and high-impact genes, but CD276 was not supported as a dominant stable core feature and remained in the target-of-interest tier. In robustness analysis, some perturbations preserved the primary interpretation, whereas others revealed transform sensitivity or an alternative high-performing feature-space solution. Conclusions: CD276 is a gene of interest associated with meningioma grade, but it was difficult to interpret it as a strong standalone predictor or a dominant stable classifier feature. In this study, the main basis of predictive performance lay not in CD276 alone but in a broader multigene transcriptomic structure, and probability output needed to be interpreted conservatively with calibration taken into account. These findings position CD276 not as a direct single-gene classifier but as a biologymotivated target-of-interest that should be interpreted within a broader transcriptomic program.

BACKGROUND: Cranioplasty is an essential procedure to restore cranial integrity, protect neural structures, and improve cosmetic outcomes. However, commercially available implants are often costly, limiting their accessibility in public healthcare systems. Three dimensional (3D) printing offers a low cost alternative for producing patient-specific solutions. METHODS: A retrospective case series of eight patients undergoing cranioplasty using customized polymethylmethacrylate (PMMA) implants fabricated with 3D printed molds was conducted. Computed tomography (CT) scans were used for segmentation and digital modeling. Patient specific molds were designed and printed preoperatively. Variables analyzed included design time, printing time, intraoperative workflow, and clinical outcomes. RESULTS: Design time ranged from approximately 1 hour for small defects to 3 hours for larger defects. Printing time ranged from 2 3 hours for smaller defects and up to 8 10 hours for larger reconstructions. Satisfactory aesthetic outcomes were achieved in 7 of 8 patients (87.5%). No major implant related complications were observed. CONCLUSION: Low cost 3D printing for PMMA cranioplasty is a feasible, accessible, and effective technique for cranial reconstruction, particularly in resource limited settings. Keywords: Cranioplasty; 3D printing; Cranial defect reconstruction; Low cost surgery; Patient specific implants; Polymethylmethacrylate; Skull reconstruction

BackgroundTherapeutic agents targeting the PD1-PDL1 interaction are of great clinical value, however accurately predicting which patients are most likely to benefit is challenging. Improved predictive biomarkers for anti-PD1 therapy are clearly needed. Quantifying PD1 saturation by PDL1 in tumor tissue has the potential to serve as such a biomarker. Here we report a novel bioassay called the PD1 Ligand Receptor Complex Aptamer (LIRECAP) assay and demonstrate it can be used to quantify the saturation of PD1 by PDL1 in formalin-fixed paraffin-embedded tumor biospecimens. ResultsThe PD1 LIRECAP assay was developed by identifying a pair of RNA aptamers. One aptamer preferentially binds to unoccupied PD1 (P aptamer) and the other to the PD1-PDL1 complex (C aptamer). P and C aptamers were added together to a formalin-fixed sample, and bound aptamer extracted. A 2-color qRT-PCR assay using a single set of primers was used to determine the ratio of the sample-bound C to P aptamers (C:P ratio) which reflected PD1 saturation by PDL1 in the sample. Quantification of PD1 saturation by PDL1 as determined by the PD1 LIRECAP assay correlated closely with PD1-mediated signaling and PD1-PDL1 proximity. Analysis of sarcoma FFPE biospecimens confirmed the assay is technically reproducible on clinical biospecimens. There were significant differences in PD1 saturation by PDL1 between patients as well as considerable intratumoral heterogeneity. ConclusionsThe PD1 LIRECAP assay is novel assay that can be used to quantify PD1 saturation by PDL1 in clinical biospecimens. The assay is technically feasible, reproducible, and has the potential to serve as a superior predictive biomarker for PD1/PDL1-based therapy. Similar assays based on this platform could be used in other systems and settings to quantify interaction between two molecules.

Background Inflammatory markers play an important role in the pathophysiology of Lumbar disc herniation (LDH). This study presents a comprehensive multi-assessment of the inflammatory landscape by combining serum inflammatory cytokines quantification, their diagnostic performance, associations with radiological features, and integrating the experimental findings into an in-silico protein-protein interaction network. Methods A multifaceted study design was utilized to quantify and compare the distribution of selected inflammatory cytokines in patients with LDH and control subjects. The diagnostic ability of these cytokines was assessed using receiver operating characteristic curve analysis. The cytokines values were correlated with selected radiological findings including disc herniation subtypes (protrusion, extrusion, and sequestration), and further categorized as contained and non-contained in patients using a Spearmans rank correlation test. Additionally, computational analysis was performed to identify the central hubs and functionally enriched pathways. Results In patients with LDH, IL-6 and IL-1{beta} showed statistically significant (IL-6: p < 0.001; IL-1{beta}: p = 0.001) rise, but IL-6 showed high diagnostic and discriminative power (AUC = 0.99; cut-off: 19.99 pg/mL). Further IL-1{beta} exhibited a positive correlation with non-contained disc herniation (extrusion and sequestration), while displaying a significant (p < 0.05) negative correlation with protrusion. In silico analysis identified IL-1{beta}, IL-8, TNF-, IL-6, IL-1, CSF2, CSF3, and IL-10 as central hubs, with IL-1{beta} being the top ranked hub in determining functionally enriched cytokine-cytokine receptor interaction. Conclusions Study confirmed IL-6 as a powerful diagnostic marker for LDH, while IL-1{beta} aids in determining contained and non-contained disc herniation. Further, IL-1{beta} was identified as the central hub, triggering functionally enriched pathways in the pathogenesis of LDH.

Waldenstrom macroglobulinemia (WM) is a rare indolent neoplasm characterized by presence of more than 10% lymphoid cells in BM that exhibit plasmacytoid or plasma cell differentiation that secretes an IgM monoclonal protein. This is a retrospective analysis of 89 patients of WM that describes the clinical and laboratory characteristics, treatment patterns and outcome of patients of WM. The median age of the entire cophort was 66 years with male predominance (67.4%). Most common presentations were symptoms pertaining to anemia (77.5%) and constitutional symptoms (33.7%). Median bone marrow lymphoplasmacytic cells were 41%. Positivity for MYD88 and CXCR4 mutations were seen in 81.8% and 2.4% cases. BR was the most common regimen used (52.8%). Overall response rates were seen at 87.8%. Median overall survival, progression free survival and time to next treatment is 8.49 years, 2.15 years and 3.88 years. BR regimen was associated with highest event free survival.

The development of chemiluminescent immunoassays (CLIAs) is a complex and iterative process that relies on costly laboratory infrastructure, limiting its accessibility and application across healthcare settings and disease areas. Here, we detail the CLIA Mobile Development Kit (CLIAMDK) a modular, mobile, and inexpensive platform to assess image sensors, smartphones and data processing workflows for CLIA development. For its demonstration, we developed two CLIAs targeting renin and aldosterone, key biomarkers for diagnosing primary aldosteronism. The results from our performance study, including 50 patient samples, demonstrate the potential of our platform in a real-world scenario. We found that the performance of our mobile reader platform is comparable to that of a state-of-the-art plate reader, with a Lower Limit-of-Detection (LLoD) approaching 41 femtomolar. We envision that our platform will help accelerate CLIA development, make it more accessible, and lay the foundations for novel, distributed, yet highly sensitive diagnostic tests.

In the tumor microenvironment (TME), dynamic interactions between cells and soluble factors promote tumor progression. We previously demonstrated that parathyroid hormone-related peptide (PTHrP), a TME-associated cytokine, enhances the aggressive phenotype of HCT116 colorectal cancer (CRC) cells, and that conditioned medium from PTHrP-treated HMEC-1 endothelial stromal cells (CM) induces epithelial-to-mesenchymal transition (EMT) in CRC cells. Here, Western blot analysis showed that CM modulates Met receptor expression and activation and promotes cancer stem cell (CSC) traits in HCT116 cells. Since PTHrP induces CPT-11 chemoresistance through Met signaling, we investigated the involvement of the CM-Met axis in this process. Viability assays revealed that CM increases cell number and confers CPT11 resistance through Met activation. Transforming growth factor beta 1 (TGF{beta}1), upregulated in PTHrP-treated HMEC-1 cells, was evaluated as a potential mediator. Its neutralization reversed the CM-induced increase in cell number but did not affect chemoresistance. In silico analyses revealed differences between CRC and normal tissues related to TGF{beta}1 signaling and Met activation, along with positive correlations among the analyzed markers. Immunohistochemical observation of human samples is consistent with our previous findings. Overall, these findings support a role for PTHrP in promoting CRC aggressiveness through coordinated effects on tumor and stromal compartments

Background: Lateral lymph node metastasis (LLNM) is associated with poor prognosis in patients with rectal cancer and may influence the indication for lateral lymph node dissection. Accurate preoperative identification of LLNM remains challenging. This study aimed to develop and internally validate a clinicoradiological model for preoperative prediction of LLNM in rectal cancer. Methods A retrospective cohort of 64 patients undergoing lateral lymph node dissection (LLND) for rectal cancer was analysed; 21 (32.8%) had pathological lateral lymph node metastasis (LLNM). A prespecified preoperative clinicoradiological model was fitted using penalised logistic regression with L2 regularisation (ridge), incorporating MRI-measured lateral lymph node short-axis diameter (LLN-SAD), dichotomised clinical T stage (T3-4 vs T1-2), dichotomised clinical N stage (N+ vs N0), and log(CA19-9+1). Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), calibration analysis, and bootstrap internal validation. Results The model showed good discrimination (AUC 0.914), with an optimism-corrected AUC of 0.887 on bootstrap validation. Calibration remained acceptable after optimism correction (calibration intercept -0.127; slope 1.045). Decision curve analysis suggested net benefit across clinically relevant threshold probabilities, particularly between 0.10 and 0.30. The model was implemented as a web-based calculator to facilitate clinical use. Conclusion This clinicoradiological model showed good discrimination, acceptable calibration, and potential clinical utility for preoperative assessment of LLNM risk in rectal cancer. It may assist individualized risk stratification and treatment planning, although external validation is required before routine clinical implementation.

Atrial fibrillation (AF) is the most prevalent cardiac arrhythmia worldwide and is strongly associated with increased risks of stroke, heart failure, and mortality. Traditional methods to predict AF and prognostic its associated risks often fail to capture the full complexity of AF patterns, limiting their predictive accuracy. In spite of the improvements achieved by machine learning (ML) techniques, state-of-the-art AF-focused predictors do not generally incorporate longitudinal data, reducing their capacity to model the dynamic and evolving nature of individual behaviors and physiological indicators over time. The absence of a longitudinal perspective restricts understanding of how AF risk develops and changes across prognostic windows. This study addresses these limitations by developing superior ML models tailored to predict adverse events within a longitudinal Portuguese cohort of individuals with AF. The work targets six clinical endpoints: stroke, all-cause death, cardiovascular death, heart failure hospitalizations, inpatient visits, and acute coronary syndrome. The predictors yielded an AUC of 0.65 for 1-year stroke prediction, outperforming CHA2DS_2-VASc (0.59). For all-cause mortality prediction, the models achieved an AUC of 0.78 against the 0.72 reference of GARFIELD-AF. In addition to predictive advances, the study identifies determinants of AF-related risks and introduces a prototype decision-support tool for clinical use.