Annals of Translational Medicine

Objectives: Among surgically resected non-small cell lung cancer (NSCLC) patients with similar stage and histopathological characteristics, there is variability in patient outcomes which highlights urgency of identifying biomarkers to predict recurrence. The goal of this study was to systematically develop a pre-surgical CT-based habitat-based radiomics classifier to predict recurrence-of-risk in NSCLC. Methods: This study included 293 NSCLC patients with surgically resected stage IA-IIIA disease that were randomly divided into a training (n = 195) and test cohorts (n = 98). From pre-surgical CT images, tumor habitats were generated using two-level unsupervised clustering and then radiomic features were calculated from the intratumoral region and habitat-defined subregions. Using ridge-regularized logistic regression, separate classifiers were developed to predict 3-year recurrence using intratumoral radiomics, habitat-based radiomics, and a combined model (intratumoral and habitat) which was generated using a stacked learning framework. For each classifier, probability of recurrence was calculated for each patient then numerous statistical and machine learning approaches were utilized to stratify patients for recurrence-free survival. Results: The combined radiomics classifier yielded a superior AUC (0.82) compared to the intratumoral (AUC = 0.75) and habitat radiomics (AUC = 0.81) models. When the classifiers were used to stratify high- versus low-risk patients utilizing a cut-point identified by decision tree analysis, high-risk patients were yielded the largest risk estimate (HR = 8.43; 95% CI 2.47 - 28.81) compared to the habitat (HR = 5.41; 95% CI 2.08 - 14.09) and intratumoral radiomics (HR = 3.54; 95% CI 1.45 - 8.66) models. SHAP analyses indicated that habitat-derived information contributed most strongly to recurrence prediction. Conclusions: This study revealed that habitat-based radiomics provided superior statistical performance than intratumoral radiomics for predicting recurrence in NSCLC.

Purpose: To identify the ocular biometric parameters associated with refractive outcomes in Chinese Primary angle closure glaucoma (PACG) patients receiving phacoemulsification and intraocular lens (IOL) implantation (PEI) surgery. Methods: 165 Chinese PACG patients receiving PEI and goniosynechialysis (GSL) and 53 cataract patients as controls only receiving PEI surgery were recruited. The prediction accuracy of IOL power calculation was assessed by the prediction error (PE), mean absolute error (MAE), median absolute error (MedAE), and proportions of eyes with a PE within {+/-} 0.25 diopters (D), {+/-} 0.50 D, {+/-} 0.75 D, and {+/-} 1.00 D. The association of different ocular biometric parameters with the PE of IOL calculation were evaluated. Results: The PACG patients had significantly higher absolute of PE as compared to the control subjects, especially the acute PACG patients. The axial length (AL), changes in aqueous depth pre- and post-surgery ({bigtriangleup}AD), and the ratio of {bigtriangleup}AD/AL were significantly associated with the PE in acute PACG patients. The association of {bigtriangleup}AD with the PE of IOL power calculation was found in PACG patients with AL [≥] 22 mm. Conclusions: This study revealed the association of AL and {bigtriangleup}AD with the PE of IOL calculation in Chinese PACG patients. Precisely predict the {bigtriangleup}AD is necessary for acute PACG patients, especially for those with AL [≥] 22 mm, to improve the refractive outcomes.

Background: To investigate the safety and efficacy of CTguided lung nodule localization needles for the preoperative localization of small pulmonary nodules. Methods: A retrospective study was conducted on 102 patients with a total of 113 small pulmonary nodules who underwent preoperative localization at Jinan Fourth People's Hospital from January 2024 to December 2025. Nodule diameter and depth, localization time, the number of pleural punctures, the localization success rate, and postoperative complications (hook dislodgement, hemorrhage, and pneumothorax) were recorded. All patients underwent video assisted thoracoscopic surgery (VATS) after localization. Results: The mean nodule diameter was 0.97{+/-}0.36 cm, the mean depth was 1.26{+/-}0.48 cm, and the mean localization time was 9.8{+/-}3.65 minutes. The hook dislodgement rate was 0.98% (1/102), the intrapulmonary hemorrhage rate was 14.71% (15/102), and the pneumothorax rate was 16.67% (17/102). All pulmonary nodules were successfully resected by VATS at 73.82{+/-}13.83 minutes after localization, and no severe complications occurred. Conclusions: The use of a CTguided lung nodule localization needle for the preoperative localization of small pulmonary nodules decreases the time needed for intraoperative nodule detection and operation time. This strategy is a simple, safe, and accurate preoperative localization method that is worthy of increased clinical use.

Background and aimTrauma healing with low-intensity ultrasound is effective for different types of injuries affecting both soft tissues and bones. The work aimed to disclose the healing potential of a new type of ultrasound, ultra-wideband low-intensity mechanical waves (UMUS), and to compare its effects with those of low-intensity pulsed ultrasound (LIPUS) in a model of trauma. Material and methodsThe work was performed on 2-to 3-month-old male Wistar rats. The model of tail amputation was created, and a transducer emitting UMUS (1-7 MHz, 0.22 mW/cm2) was applied daily for 10 days to the surface of the trauma site in animals that were timely immobilized. LIPUS (1.5 mHz, 30.0 mW/cm2) was used in a separate group of animals. Sham-stimulated rats were used as a control. The intensity of collagen expression in the subdermal tissue was assessed in van Gieson-stained sections, whereas in the UMUS group, expression of CD31, CD34, VEGF, and Ki67 was analyzed. ResultsStarting on the 20th day after trauma, UMUS-treated animals demonstrated a statistically significant decrease in the surface area of the traumatic zone compared to the control, whereas LIPUS-treated rats showed this difference on the 30th day of observation. Starting from the 30th day, a significantly greater reduction in the surface of trauma was observed in UMUS, with complete closure achieved in 6 out of 9 rats (P=0.019 vs control), whereas in LIPUS-treated animals, a similar result was observed in 2 out of 8 rats (P>0.05). In UMUS-treated rats, heightened expression of collagen in animals with LIPUS exceeded control data by 7.84% (P=0.034), while the expression in rats with UMUS exceeded data in LIPUS-treated rats by 14.71% (P=0.013). Increased expression of CD31, CD34, VEGF, and Ki67 was observed in UMUS-treated rats. ConclusionsUMUS treatment accelerated healing and reduced wound size, and increased the expression of collagen, CD31, VEGF, CD34, and Ki67, supporting angiogenesis and collagen formation. Effects are more pronounced compared to LIPUS treatment. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=176 HEIGHT=200 SRC="FIGDIR/small/717366v1_ufig1.gif" ALT="Figure 1"> View larger version (75K): org.highwire.dtl.DTLVardef@1b82f66org.highwire.dtl.DTLVardef@12ffd81org.highwire.dtl.DTLVardef@1ac385aorg.highwire.dtl.DTLVardef@1a7da17_HPS_FORMAT_FIGEXP M_FIG C_FIG

Objective: To describe the clinical characteristics of term neonates with neonatal bacterial meningitis (NBM) and explore the association between different pathogens and imaging complications, providing clinical evidence for early identification and individualized management. Methods: A retrospective study was conducted on 531 term neonates diagnosed with NBM admitted to the Capital Institute of Pediatrics from 2013 to 2025. Demographics, clinical manifestations, laboratory parameters, etiological results, imaging complications and treatment measures were collected. Patients were divided into favorable/adverse discharge outcome groups and pathogen-positive/negative groups. Statistical analyses were performed using appropriate tests, and Cramers V coefficient was used to analyze the association between pathogens and imaging complications. Results: (1) The most common clinical manifestations were abnormal body temperature (79.85%), altered consciousness (55.18%) and jaundice (46.52%). CSF/blood culture was positive in 133 cases (25.05%), with Escherichia coli (27.07%), group B streptococcus (17.29%) and Staphylococcus species (16.54%) as predominant pathogens. The overall incidence of imaging complications was 22.22%, mainly hydrocephalus (5.84%), subdural effusion (4.90%) and encephalomalacia (2.64%). (2) Adverse discharge outcomes occurred in 107 cases (20.15%). Compared with the favorable group, the adverse group had higher incidences of convulsions, altered consciousness, anterior fontanelle bulging, abnormal muscle tone and primitive reflexes (all P<0.001), more obvious laboratory abnormalities (higher CRP, CSF leukocytes and protein, lower CSF glucose, all P<0.05), higher culture positive rates and greater need for adjuvant therapy (all P<0.001). (3) Pathogen-positive patients had higher imaging complication rates. Gram-negative infections were associated with higher hydrocephalus and subdural effusion rates, while Gram-positive infections had higher brain abscess risk. Specifically, Escherichia coli correlated with hydrocephalus and subdural effusion; group B streptococcus with cerebral infarction and encephalomalacia; LM with intracranial hemorrhage and brain abscess; negative cultures correlated with no imaging complications (all P<0.05). Conclusion: Term NBM neonates have non-specific manifestations, mainly abnormal body temperature and altered consciousness. Predominant pathogens are Escherichia coli, group B streptococcus and Staphylococcus species, with hydrocephalus and subdural effusion as common imaging complications. Adverse outcomes are associated with severe symptoms, obvious laboratory abnormalities and higher pathogen positivity. Specific pathogens correlate with distinct imaging complications.

BackgroundIt has been demonstrated that stem cell transplantation promotes healing of the infarcted heart through paracrine effects. However, the therapeutic potential of exosomes secreted by hiPSC-derived epicardial cells (hEP-Exos) for treating infarcted hearts remains unclear. Myocardial infarction (MI) can trigger EP activation, increasing EP paracrine function. Therefore, this study aims to determine and compare the cardioprotective effects of exosomes secreted by hEPs under normoxic (Exo-N) and hypoxic (Exo-H) conditions in MI mice and to explore the underlying mechanisms. MethodsTwo types of exosomes were collected by ultracentrifugation and delivered via intramyocardial injection in a murine MI model. The protective effects of Exo-N and Exo-H on the infarcted heart were assessed using echocardiography, histological examination, and immunofluorescence analysis. Additionally, microRNA sequencing, luciferase activity assays, and miRNA gain-and loss-of-function experiments were performed to identify enriched miRNAs and investigate their roles in different exosome populations. ResultsIn vitro, both Exo-N and Exo-H enhanced the migration and tube-formation capacities in human umbilical vein endothelial cells (HUVECs) and reduced the apoptosis in hiPSC-derived cardiomyocytes (hCMs) under oxygen-glucose deprivation (OGD), with Exo-H exhibiting a stronger effect. In vivo, both Exo-N and Exo-H significantly improved contractile function, reduced infarct size, and mitigated adverse remodeling in mouse hearts with MI, accompanied by increased cardiomyocyte survival and angiogenesis, with Exo-H showing superior efficacy. Mechanistically, miRNA sequencing revealed distinct cargo profiles between Exo-N and Exo-H. miR-214-3p was identified as a key mediator of the enhanced therapeutic potency of Exo-H. miR-214-3p promoted EC angiogenesis by suppressing vasohibin-1 and attenuated cardiomyocyte mitochondrial fission and apoptosis by suppressing mitochondrial elongation factor 2 (MIEF2). ConclusionsThis study demonstrates that administration of hEP-Exos, particularly Exo-H, provides robust cardioprotection by enhancing cardiomyocyte survival and angiogenesis, potentially mediated by miR-214-3p. These findings suggest that conditioned hEP-Exos could be a promising and effective acellular therapeutic option for treating MI.

Background: Extrauterine growth restriction (EUGR) is a common and clinically significant complication among preterm infants, contributing to adverse neurodevelopmental and metabolic outcomes. Early and individualized risk prediction remains challenging. This study aimed to develop and validate an interpretable machine learning model for early prediction of EUGR using routinely available clinical variables, and to implement a user-friendly web-based calculator for clinical use. Methods: We retrospectively analyzed 1,431 preterm infants admitted within 24 hours after birth to our hospital between May 2020 and March 2025. Infants from the Yangpu campus (n=863) formed the training set, and those from the Huangpu campus (n=568) formed the validation set. Early clinical variables available within 48-72 hours were screened using the Boruta algorithm. Logistic regression, XGBoost, random forest, decision tree, and support vector machine models were developed and compared. Model performance was evaluated using area under the curve (AUC), accuracy, sensitivity, specificity, F1 score, and Brier score. SHapley Additive exPlanations (SHAP) were applied to assess global and individual feature contributions, nonlinear effects, and interactions. A web-based calculator was constructed based on the optimal model. Results: Nine variables were identified as important predictors: birth weight, small for gestational age status, gestational age, breastfeeding, multiple gestation, neonatal respiratory distress syndrome, patent ductus arteriosus, maternal hypertension, and maternal group B Streptococcus infection. Among the five models, XGBoost achieved the best performance in the validation set (AUC 0.922, accuracy 0.849, Brier score 0.108). SHAP analysis showed that low birth weight, small for gestational age, maternal group B Streptococcus infection, and patent ductus arteriosus were major risk factors, while breastfeeding was protective. Notable nonlinear and interactive effects were observed, particularly between birth weight and gestational age and between breastfeeding and patent ductus arteriosus. The web-based calculator provides real-time individualized risk estimation and visualized interpretation. Conclusions: An interpretable XGBoost-based model and web calculator were successfully developed and validated for early prediction of EUGR in preterm infants. This tool may support clinicians in identifying high-risk infants and guiding individualized nutritional and clinical management.

Abstract Background Diabetic Retinopathy (DR) is one of the leading cause of vision loss and blindness. AI models have been instrumental in providing an alternative solution to real-life medical treatment which are costly and sometimes not readily available in developing and underdeveloped nations. However, most of the existing AI models are developed with high-quality clinical images that makes it difficult to use such models in low-resource settings. For this reason, this research focus on bridging this gap by developing a low-resource, mobile-friendly, and deployable deep learning (DL) model for the detection of DR using an imbalance-aware optimal transport (OT) learning approach. Methods We trained our proposed framework with both high-quality hospital- grade images and low-resource smartphone-acquired images, and evaluated with the original test set from the smartphone domain. We also curated three levels of smart- phone image-degradation quality and reported results from multiple experiments with bootstrapping. All model evaluations were assessed using the AUC, Sensitivity, and Specificity. Our results were compared with empirical risk minimization (ERM), Prototype OT, and Sinkhorn OT methods. Results We used four strong backbone architectures in the assessment. With our framework, Mobilevit-s achieved the best performance: an AUC of 87%, sensitivity of 89%, and specificity of 95%. Meanwhile, the statistical significance performance test (95% CI) shows that the AUC results are in the range of approximately 84% to 89%. For sensitivity, the range is 81% to 96%, and for specificity, 93% to 96%. This result indicated a performance increase of more than 3-5% compared to baseline methods. Conclusion Our framework shows promising results for low-resource DR screening, which has a potential to benefit less-advantaged groups and developing nations. Keywords Diabetic retinopathy, cost-effective AI, optimal transport, smartphone screening, deep learning.

Purpose: To develop an interpretable feature-based Deep Parametric Response Mapping (PRMD) method that combines wavelet scattering convolution networks and machine learning to spatially detect and quantify functional small airways disease (fSAD) and emphysema on paired inspiratory-expiratory CT scans, with enhanced noise robustness. Materials and Methods: In this retrospective analysis of prospectively acquired data (2007-2017), we developed and validated a deep learning-based PRM approach using paired CT scans from 8,972 tobacco-exposed COPDGene participants ([&ge;]10 pack-years; mean age 60.1 {+/-} 8.8 years; 46.5% women), including controls with normal spirometry (n = 3,872; controls), PRISm (n = 1,089), GOLD 1-4 COPD (n = 4,011). Data were stratified into training, validation, and testing sets (24:6:70). PRMD extracts translation-invariant image features using a wavelet scattering network and applies a subspace learning classifier to classify voxels as emphysema or non-emphysematous air trapping (fSAD). PRMD was compared with conventional density-based PRM for voxel-wise agreement, correlation with pulmonary function, robustness to noise, and sensitivity to misregistration using Pearson correlation, Bland-Altman analysis, and paired t tests. Results: PRMD achieved 95% voxel-wise agreement with standard PRM (r = 0.98) while demonstrating significantly greater robustness under noise. PRMD showed stronger correlations with FEV1; (emphysema: r = - 0.54; fSAD: r = - 0.51; P < 0.0001) than standard PRM (r = - 0.42 for both; P < 0.0001). Under simulated high-noise conditions, standard PRM overestimated disease by ~15%, whereas PRMD limited error to < 5% (P < 0.001). Conclusion: PRMD provides an interpretable, feature-driven and noise-resilient alternative to traditional PRM for emphysema and fSAD classification, enhancing the reliability of CT-based COPD phenotyping for multi-center studies and low-dose imaging applications.

Background: Immune checkpoint inhibitors (ICIs) have revolutionized cancer therapy but can cause serious immune-related adverse events (irAEs), with pneumonitis (ICI-P) being among the most severe. Early identification of high-risk patients before ICI initiation is critical for closer monitoring, timely intervention, and improved outcomes. Purpose: To develop and validate a deep learning foundation model to predict ICI-P from baseline CT scans in patients with lung cancer. Methods: We designed the Checkpoint-Inhibitor Pneumonitis Hazard EstimatoR (CIPHER), a deep learning foundation model that combines contrastive learning with a transformer-based masked autoencoder to predict ICI-P from baseline CT scans in patients with lung cancer. Using self-supervised learning, CIPHER was pre-trained on 590,284 CT slices from 2,500 non-small cell lung cancer (NSCLC) patients to capture heterogeneous lung parenchymal patterns. After pre-training, the model was fine-tuned on an internal NSCLC cohort for ICI-P risk prediction, using images from 254 patients for model development and 93 patients for internal validation. We compared CIPHER with classical radiomic models and further evaluated it on an external NSCLC cohort of 116 patients. Results: In the internal immunotherapy cohort, CIPHER consistently distinguished patients at elevated risk of ICI-P from those without the event, with AUCs ranging from 0.77 to 0.85. In head-to-head benchmarking, CIPHER achieved an AUC of 0.83, outperforming the radiomic models. In the external validation cohort, CIPHER maintained strong performance (AUC = 0.83; balanced accuracy = 81.7%), exceeding the radiomic models (DeLong p = 0.0318) and demonstrating higher specificity without sacrificing sensitivity. By contrast, the radiomic model showed high sensitivity (85.0%) but markedly lower specificity (45.8%). Confusion matrix analysis confirmed the robust classification performance of CIPHER, correctly identifying 80 of 96 non-ICI-P cases and 16 of 20 ICI-P cases. Conclusions: We developed and externally validated CIPHER for predicting future risk of ICI-P from pre-treatment CT scans. With prospective validation, CIPHER may be incorporated into routine patient management to improve outcomes.

BackgroundThe COVID-19 pandemic exposed critical shortages of mechanical ventilators, particularly in low-resource settings. Disruptions in global supply chains and dependence on specialized components highlighted the need for scalable, locally manufacturing alternatives for emergency respiratory support. AimTo describe and evaluate a simplified, supply-chain-independent mechanical ventilator assembled from widely available automotive and simple hardware components, and intended as a last-resort solution. MethodsThe ventilator is based on a reciprocating air pump driven by an automotive windshield wiper motor coupled to parallel shaft bellows and readily assembled passive membrane valves, only requiring materials available from standard hardware retailers, minimal tools, and basic manual skills. Ventilator performance was assessed through bench testing using a patient model simulating severe lung disease in an adult (R=20 cmH2O{middle dot}s/L, C=15 mL/cmH2O) and pediatric (R=50 cmH2O{middle dot}s/L, C=10 mL/cmH2O) patients. Realistic proof of concept was performed in four mechanically ventilated 50-kg pigs. ResultsThe device delivered tidal volumes up to 600 mL and respiratory rates up to 45 breaths/min with PEEP up to 10 cmH2O, covering pediatric and adult ventilation ranges. In vivo testing showed that the ventilator maintained arterial blood gases within the targeted range. Technical details for ventilator construction are provided in an open-source video tutorial. DiscussionThis low-cost ventilator demonstrated adequate performance under demanding conditions. Although not a substitute for commercial intensive care ventilators, its simplicity, autonomy, and independence from fragile supply chains provide a potentially life-saving option in resource-constrained emergency scenarios.

Purpose:To improve determination of the treatment area for the personalization of subliminal transscleral cyclophotocoagulation (SL-TSCPC) procedures in glaucoma treatment, we designed a biometry based model of the human eye to find the estimated cilary body (CB) arc length (ECBAL) and the calculated CB distance (CCBD). Methods: We developed a rotationally symmetric modified two-sphere eye model based on axial length (AL), mean keratometry (mean K), anterior chamber depth (ACD), lens thickness (LT), and white-to-white (WTW). ECBAL and CCBD were calculated for each eye. Fluence was calculated with standardized parameters. Results: Publicly accessible biometric measurements for 24,001 eyes were pooled for analysis. The mean ECBAL was 23.99+-1.8 mm. The correlations of ECBAL with AL and ACD were 0.723 and 0.754 respectively (p < 0.01). The number of eyes with an ECBAL 21.7-22.0 mm was 131 of 24,001 (0.55%). The mean CCBD was 4.21+-0.8 mm. The number of eyes with a CCBD of 3.8 mm was 1,445 of 24,001 (6.02%). Mean fluence was 120.33+-9.0 J/cm2. A mean difference of -8.18+-6.9%, ranging from -22.66% to +29.07% in fluence was observed with treating only the recommended 22 mm versus the ECBAL. Conclusions: This study demonstrated that use of 22.0 mm as the standard treatment arc length may under or overdose laser treatment in many eyes. Precise estimation or exact localization of the CB treatment area is required to accurately calculate fluence. Translational Relevance:The model proves that CB arc length is a variable while current guidelines consider it a constant

Background: Patient-ventilator synchrony, an essential prerequisite for non-invasive mechanical ventilation, requires an accurate matching of every phase of the respiration between patient and the ventilator. Methods: We developed a long short-term memory (LSTM)-based model that can predict the inspiratory and expiratory time of the patient. This model consisted of two hidden layers, each with eight LSTM units, and was trained using a dataset of approximately 27000 of 500-ms-long flow signals that captured both inspiratory and expiratory events. Results: The LSTM model achieved 97% accuracy and F1 score in the test data, and the average trigger error was less than 2.20%. In the first trial, 10 volunteers were enrolled. In "Compliance" mode, 78.6% of the triggering by the LSTM model was compatible with neuronal respiration, which was higher than Auto-Trak model (74.2%). Auto-Trak model performed marginally better in the modes of pressure support = 5 and 10 cmH2O. Considering the success in the first clinical trial, we further tested the models by including five patients with acute respiratory distress syndrome (ARDS). The LSTM model exhibited 60.6% of the triggering in the 33%-box, which is better than 49.0% of Auto-Trak model. And the PVI index of the LSTM model was significantly less than Auto-Trak model (36.5% vs 52.9%). Conclusions: Overall, the LSTM model performed comparable to, or even better than, Auto-Trak model in both latency and PVI index. While other mathematical models have been developed, our model was effectively embedded in the chip to control the triggering of ventilator. Trial registration: Approval Number: 2023ZDSYLL348-P01; Approval Date: 28/09/2023. Clinical Trial Registration Number: ChiCTR2500097446; Registration Date: 19/02/2025.

Purpose: To evaluate whether chest radiograph-derived age acceleration is associated with incident lung cancer and whether it improves discrimination beyond established lung cancer risk factors. Materials and Methods: This retrospective analysis used prospectively collected data from the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial. Baseline digitized chest radiographs from the initial screening year were analyzed using a previously validated deep learning model that estimates chest radiograph-derived age (Xp-age). Age acceleration (AgeAccel) was defined as the residual of Xp-age after calibration to chronological age using a regression model from the development dataset. A 1-year landmark design excluded participants diagnosed with lung cancer or censored within 1 year of baseline. Associations with incident lung cancer were assessed using multivariable Cox proportional-hazards models adjusted for prespecified demographic and clinical predictors, including smoking variables used in the PLCOm2012 risk prediction model. Discrimination was evaluated using the concordance index and 6-year time-dependent area under the receiver-operating-characteristic curve. Results: The analytic cohort included 23,213 participants (mean age, 62.5 years); 790 developed incident lung cancer after the landmark (mean follow-up, 16.7 years). Higher AgeAccel was associated with increased lung cancer incidence (hazard ratio, 1.10 per 1-SD increase; 95% confidence interval: 1.03- 1.17); however, addition of AgeAccel to an established risk factor model resulted in minimal change in discrimination (C-index, 0.840 vs. 0.839; time-dependent AUC at 6 years, 0.852 vs. 0.852). Attribution maps emphasized the aortic arch/mediastinal region with similar spatial patterns across smoking and lung cancer strata. Conclusion: Chest radiograph-derived age acceleration was independently associated with future lung cancer incidence.

Background: Paediatric pneumonia is a leading cause of childhood morbidity and mortality worldwide. Chest X-rays (CXR) are an important diagnostic tool in the diagnosis of pneumonia, but shortages in specialist radiology services lead to clinically significant delays in CXR reporting. The ability to communicate findings both to clinicians and laypersons allows MLLMs to be deployed throughout clinical workflows, from image analysis to patient communication. However, MLLMs currently underperform state-of-the-art deep learning classifiers. Objective: To evaluate the diagnostic accuracy of ensemble strategies with MLLMs compared to the baseline average agent for paediatric radiological pneumonia detection. Methods: We conducted a retrospective cohort study using paediatric CXRs from two independent hospital datasets totalling 2300 CXRs. Fifteen MedGemma-4B-it agents independently classified each CXR into five pneumonia likelihood categories. Majority voting, soft voting, and GPTOSS-20B aggregation were compared against the average agent performance. The primary metric evaluated was OvR AUROC. Secondary metrics included accuracy, sensitivity, specificity, F1-score, Cohen's kappa, and OvO AUROC. Results: Soft voting achieved improvements in OvR AUROC (p_balanced = 0.0002, p_real-world = 0.0003), accuracy (p_balanced = 0.0008, p_real-world < 0.0001), Cohen's Kappa (p_balanced = 0.0006, p_real-world = 0.0054) and OvO AUROC (p_balanced < 0.0001, p_real-world = 0.0011) across both datasets, and a superior F1-value (pbalanced = 0.0028) for the balanced dataset. Conclusion: Soft voting enhances MedGemma's diagnostic discriminatory performance for paediatric radiological pneumonia detection. Our system enables privacy-preserving, near real-time clinical decision support with explainable outputs, having potential for integration into emergency departments. Our system's high specificity supports triage by flagging high-risk radiological pneumonia cases.

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.

Background: Chronic Obstructive Pulmonary Disease (COPD) is the third leading cause of global mortality, with persistent lung inflammation contributing to disease progression. This inflammation is partly associated with reduced levels of histone deacetylase 2 (HDAC2). Previous studies suggest that Vitamin D may modulate HDAC2 levels. This study aimed to evaluate the effect of Vitamin D supplementation on HDAC2 expression in stable COPD patients. This experimental study aimed to evaluate the effect of vitamin D supplementation on HDAC2 expression in stable COPD patients at Jemursari Islamic Hospital. Methods: Five COPD patients received a daily dose of 5000 IU of Vitamin D for three months. Serum levels of 25(OH)D3 and HDAC2 were measured before and after the intervention. Results: Vitamin D supplementation resulted in a significant increase in both 25(OH)D and HDAC2 levels. Pulmonary function parameters showed an increasing trend, however, no statistically significant differences were observed. Conclusion: Vitamin D supplementation was associated with increased HDAC2 levels, suggesting a potential anti-inflammatory effect. However, no significant improvement in pulmonary function was observed. Further studies are needed to determine its clinical impact.

Radiation-induced intestinal injury is a widely used model for studying mechanisms regulating tissue injury and regeneration. Traditionally, Cesium (137Cs) radiation has been used in research applications, but over the past decade, X-ray irradiation has become increasingly favored due to its improved safety and non-radioactive profile. Since each type of radiation has distinct physical characteristics that drive its performance, we sought to systematically compare the effects of the X-ray and 137Cs irradiators on intestinal epithelial injury and regeneration. Using established in vitro models, including colorectal cancer cell lines such as HCT116, RKO, and DLD-1, and mouse intestinal organoids, alongside an in vivo model, Bmi1-CreER;Rosa26eYFP, we evaluated differences in transcriptional, protein, and histopathological responses to irradiation. Our results demonstrate that X-ray produced intestinal injury and regenerative responses comparable to those induced by 137Cs, supporting its reliability as an alternative modality for studying intestinal radiation.

Introduction Cervical spondylotic myelopathy (CSM) surgery is frequently associated with residual neurological deficits, partly due to unrecognized dynamic spinal cord compression on conventional MRI. Current static imaging may miss position-dependent stenosis, resulting in insufficient or inappropriate decompression. This study aims to evaluate whether dynamic MRI-guided individualized surgery improves neurological outcomes compared to conventional MRI-based planning. Objectives This study aims to examine the association between dynamic MRI-guided surgical planning and neurological recovery in cervical spondylotic myelopathy, and to evaluate its role in identifying responsible segments, avoiding excessive surgery, and improving clinical outcomes. Methods This single-center retrospective cohort study will include 300 patients who underwent cervical spine surgery between January 2020 and December 2025 at the First Affiliated Hospital of Guangxi University of Chinese Medicine. Patients will be categorized into the dynamic MRI-guided group (n=150) or conventional MRI-based group (n=150) based on preoperative imaging modality. 1:1 propensity score matching will be performed using age, sex, BMI, disease duration, baseline mJOA score, and number of compressed segments. The primary outcome is the rate of improvement in the mJOA score at 6 months postoperatively. Secondary outcomes include VAS, NDI, reoperation rate, and time to first complication. Between-group comparisons will use t-tests/Mann-Whitney U tests for continuous variables, {chi}{superscript 2} tests/Fisher's exact tests for categorical variables, and Kaplan-Meier estimates with the log-rank test for time-to-event outcomes. A two-sided P<0.05 will be considered significant. Analyses will be performed using R software (version 4.4.1). Ethical approval was obtained from the Medical Ethics Committee of the First Affiliated Hospital of Guangxi University of Chinese Medicine (Approval No. 2025-080-KY-01) from February 06, 2026 to February 05, 2027. Expected outcomes We hypothesize that dynamic MRI-guided surgical planning will improve neurological recovery and decompression accuracy in cervical spondylotic myelopathy, providing evidence for optimized preoperative imaging and precision spine surgery.

BackgroundPatent ductus arteriosus (PDA) is a common and potentially serious cardiovascular condition in preterm infants, particularly those with low gestational age and birth weight. Its management remains controversial due to variability in screening, diagnostic criteria, and treatment strategies. This study aimed to evaluate risk factors, outcomes, and management strategies for PDA in preterm infants, and to identify predictors of clinical and echocardiographic response to therapy. MethodsWe conducted a retrospective cohort study over a 4-year period (2016-2019) in the neonatal intensive care unit (NICU) of a tertiary care center. All consecutive preterm infants admitted during the study period were eligible. Infants with echocardiographically confirmed PDA who received pharmacological treatment with intravenous paracetamol or ibuprofen were included in the analysis. Missing data were minimal and handled using available-case analysis. Statistical analyses included descriptive statistics, Pearsons chi-square test, and multivariable logistic regression. ResultsAmong 2154 preterm infants admitted to the NICU, 60 were diagnosed with PDA (incidence : 2.8%). The mean gestational age was 29 {+/-} 2.6 weeks, and the median birth weight was 1200 g. Respiratory distress occurred in 95% of cases, mainly due to hyaline membrane disease (86.7%). PDA was symptomatic in 80% of infants. First-line treatment resulted in clinical improvement in 77% and ductal closure in 83.3% of cases, most within 3 days. Predictors of successful closure included gestational age [&ge;] 28 weeks (OR = 5.9; 95% CI : 1.7-20.2) and antenatal corticosteroid exposure (OR = 1.2; 95% CI : 1.0-1.6). Overall mortality was 35% and was significantly higher in infants < 28 weeks (OR = 5.0; 95% CI : 2.4-10.3). Clinical improvement (OR = 3.7) and echocardiographic closure (OR = 4.5) after first-line treatment were associated with reduced mortality. ConclusionsPDA in preterm infants is associated with substantial morbidity and mortality, particularly in those born before 28 weeks of gestation. Early diagnosis, antenatal corticosteroid exposure, and timely pharmacological treatment may improve outcomes. Systematic echocardiographic screening in high-risk neonates should be considered.