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PubMed2026

Applications of quantum AI in brain disorder diagnosis: A systematic review.

BACKGROUND AND OBJECTIVE: Brain disorder diagnosis and prediction remain challenging because neuroimaging, electrophysiological, behavioral, and multimodal data are high-dimensional, noisy, heterogeneous, and limited by small clinical cohorts. This systematic review synthesised applications of quantum artificial intelligence (QAI) for brain disorder diagnosis, prediction, detection, and monitoring. METHODS: Following PRISMA guidelines, studies published from 2016 to 13 January 2026 were retrieved from Scopus, Web of Science, and IEEE Xplore. After screening, 36 studies met the eligibility criteria and were qualitatively analysed according to disorder category, data modality, QAI method, implementation setting, validation strategy, and performance. RESULTS: At the broader disease-group level, neurodegenerative disorders were the most frequently investigated, followed by mental health and psychiatric disorders. At the individual level, Parkinson's disease and schizophrenia were the leading applications, followed by depression, anxiety, Alzheimer's disease, and stress-related tasks. MRI-based modalities were the most frequently used data source, followed by multimodal data and EEG. Methodologically, primary QAI approaches were dominated by quantum neural and QDL architectures, followed by quantum-inspired optimization or feature-selection methods and quantum-kernel/conventional QML classifiers. Qiskit/IBM Quantum and PennyLane were the most frequently reported quantum software frameworks. However, most studies relied on simulators, classical quantum-inspired implementations, or unclear implementation settings, with limited real-hardware evaluation. CONCLUSIONS: QAI shows emerging potential for brain disorder analysis, particularly through hybrid quantum-classical learning, quantum neural architectures, quantum-kernel methods, and quantum-inspired optimization. Nevertheless, current evidence remains preliminary and requires larger datasets, subject-level and external validation, fair classical benchmarking, noise-resilient circuits, real quantum hardware evaluation, explainability, and clinical validation.

Humans↗

PubMed2026

Effect of Food on Balcinrenone/Dapagliflozin Pharmacokinetics and the Pharmacokinetics of Balcinrenone When Dosed with a P-gp Inhibitor.

Balcinrenone (AZD9977) is a novel selective non-steroidal mineralocorticoid receptor antagonist with a distinct mode of action being developed as a fixed-dose combination with the sodium-glucose cotransporter-2 inhibitor dapagliflozin for the treatment of heart failure with impaired kidney function, and chronic kidney disease. In this Phase 1 randomized open-label three-way crossover study we investigated the effect of food on balcinrenone/dapagliflozin pharmacokinetics, and the pharmacokinetics of balcinrenone when dosed with a P-glycoprotein (P-gp) inhibitor. Fourteen healthy participants were administered an oral capsule of balcinrenone/dapagliflozin 40 mg/10 mg in three dosing periods: fasted (reference), fed (high-fat, high-calorie meal) and with a P-gp inhibitor (quinidine 300 mg × 2). Balcinrenone exposure was comparable in the fed and fasted states (geometric mean ratios [GMRs] [90% CI]: maximum plasma concentration [Cmax] 1.05 [0.88, 1.25]; area under the plasma concentration-time curve from time 0 to infinity [AUCinf] 1.12 [1.06, 1.19]). In the fed state, dapagliflozin AUCinf was comparable to the fasted state (GMR [90% CI] 1.05 [1.01, 1.09]), whereas Cmax was decreased (GMR [90% CI] 0.59 [0.51, 0.69]), in line with previous dapagliflozin food interaction studies. Co-administration with quinidine increased balcinrenone exposure: GMRs (90% CI) 1.48 (1.24, 1.76) and 1.24 (1.17, 1.31) for Cmax and AUCinf, respectively, but AUC fold increase was <2, the level used for classification of sensitive P-gp substrates. All interventions were well tolerated. In conclusion, this study supports dosing of balcinrenone/dapagliflozin without regard to food. Balcinrenone is not considered a sensitive P-gp substrate. No P-gp based dosing precautions are warranted based on this study.

Adult↗

PubMed2026

Clinical pharmacokinetics of afatinib: A systematic review.

BACKGROUND: Afatinib is commonly used in the treatment of non-small cell lung cancer (NSCLC). This systematic review summarizes clinical pharmacokinetics (PK) evidence focusing on the effect of disease state and drug interactions on afatinib exposure. METHODS: Google Scholar, Science Direct, PubMed, and the Cochrane library were searched for human studies reporting the clinical PK of afatinib. The search yielded 24 articles that met the predefined inclusion criteria. RESULTS: Afatinib exposure increased slightly more than dose proportionally, with higher doses producing greater AUC0-24 and Cmax values. The apparent oral clearance reported after administration of the oral solution was lower than that observed following tablet administration. The Cmax of afatinib increases by 38.5% after coadministration with ritonavir and exposure decreases 34.3% with rifampicin. The Cmax decreases 31.45% when given with pemetrexed. Both the AUC0-24 and Cmax increase in NSCLC and tumor state. The AUC0-24 of afatinib is 2.61 folds higher following multiple oral doses among patients with solid tumors. Afatinib exposure is 22.1 % higher in renal impaired patients than in healthy controls. In grade 2 diarrhea, the AUC0-24 of afatinib is 83.93% higher as than in grade 0-1 diarrhea in solid tumor patients. CONCLUSION: This systematic review provides an updated synthesis of clinical PK evidence on afatinib. Afatinib exposure is influenced by dose, repeated administration, renal impairment, diarrhea associated toxicity, and P-glycoprotein mediated drug interactions. These findings may support individualized dosing, toxicity-guided dose adjustment, and future development of PK models for afatinib.

Humans↗

PubMed2026

Multimodal alignment improves generalizability of genomic biomarker prediction in computational pathology.

Computational pathology models that use digitized histopathology whole-slide images have the potential to become a cost-effective and scalable alternative to molecular assays for the prediction of genomic biomarkers, a key task in precision oncology. However, as new genomic biomarkers are discovered or quantified, large, labeled datasets must be prospectively collected to train new models. To address this challenge, we developed multimodal alignment for biomarker learning and generalization (MARBLE), a multimodal contrastive pretraining strategy that integrates structured biomarker knowledge into representation learning of histopathology images. MARBLE aligns histopathology-derived representations with representations of genomic biomarkers generated by a large language model (LLM) and a protein language model (PLM). This biologically informed alignment enables data-efficient generalization to novel, out-of-distribution biomarkers. Using the MSK-IMPACT cohort of over 40,000 patients across multiple biomarker panel versions, we design experiments grounded in real-world data to demonstrate the value of our proposed approach.

CP: computational biology↗

PubMed2026

Angiography-Based Index of Microcirculatory Resistance in Assessing the MVO and Infarct Size in STEMI Patients.

OBJECTIVES: To evaluate angiography-based index of microcirculatory resistance (angio-IMR) in assessing microvascular obstruction (MVO) and infarct size (IS) in ST-segment elevation myocardial infarction (STEMI). BACKGROUND: The effect of thrombolysis on post-percutaneous coronary intervention (PCI) angio-IMR, and its associations with MVO and IS remains unclear. METHODS: One hundred twenty-three STEMI patients randomized to receive 5 mg intravenous bolus of recombinant staphylokinase (r-SAK) or normal saline (NS) before PCI were recruited. Angio-IMR was computed in infarct-related arteries. MVO and IS were detected by cardiac magnetic resonance imaging. RESULTS: Compared with NS group, r-SAK group exhibited numerically lower post-PCI angio-IMR (39.12 U vs. 42.57 U; p = 0.567), MVO (54.0% vs. 70.9%; p = 0.059), MVO extent (0.70% vs. 1.90%; p = 0.101) and IS (21.30% vs. 24.50%; p = 0.079). Post-PCI angio-IMR was positively correlated with MVO extent (ρ = 0.347; p < 0.001) and IS (ρ = 0.324; p < 0.001). Receiver operating characteristic analyses showed moderate diagnostic performance of angio-IMR for MVO (area under the curve [AUC] = 0.750; p < 0.001), MVO > 2.6% (AUC = 0.735; p < 0.001) and IS > 25% (AUC = 0.712; p < 0.001). The exploratory optimal cut-off values for these endpoints were approximately 40 U. CONCLUSIONS: In STEMI patients, a single bolus of r-SAK before PCI was associated with numeric reductions in post-PCI angio-IMR, MVO, MVO extent and IS. Additionally, angio-IMR exhibited a significantly positive correlation with both MVO extent and IS, demonstrating the diagnostic value of this wire-free method for assessing microvascular injury.

Humans↗

PubMed2026

Application of SPI-guided analgesia in laparoscopic gynecologic surgery: a randomized controlled trial evaluating the remifentanil-sparing effect and predictive value of time-weighted SPI.

This study aimed to achieve two primary objectives: (1) to evaluate the opioid-sparing effect of Surgical Pleth Index (SPI)-directed analgesia during surgery via a randomized controlled trial (RCT), and (2) to propose and preliminarily assess a novel dynamic metric, Threshold-based Time-Weighted SPI (Tb-TW-SPI), which integrates stimulus intensity and duration, for its predictive efficacy regarding postoperative moderate-to-severe pain. Employing an RCT combined with exploratory analysis, 61 patients undergoing elective laparoscopic gynecologic surgery were randomized into an SPI-directed analgesia group or a conventional analgesia group. The primary outcome was total intraoperative remifentanil consumption. Postoperatively, an exploratory analysis of the control group data evaluated the correlation between Tb-TW-SPI and Numeric Rating Scale (NRS) pain scores in the post-anesthesia care unit (PACU), calculating its predictive value for moderate-to-severe pain (NRS ≥ 4). Results: The SPI-directed group required significantly less intraoperative remifentanil than the conventional group [median (IQR): 5.84(5.02,6.62)vs. 6.96(5.81,8.19)µg/kg/h; P = 0.016]. Postoperative pain scores did not differ significantly between groups (P > 0.05). Exploratory analysis of the conventional analgesia group revealed that Tb-TW-SPI values were significantly higher in patients with moderate-to-severe postoperative pain (NRS ≥ 4) compared to those without (P = 0.0417).The area under the ROC curve for Tb-TW-SPI predicting this pain was 0.74 (95% CI: 0.52-0.96), with 67% sensitivity and 76% specificity at an optimal cutoff of 1210. This RCT suggests that SPI-directed analgesia can safely and moderately reduce intraoperative remifentanil consumption. Furthermore, the proposed Tb-TW-SPI metric, in this exploratory analysis, suggests potential for predicting postoperative pain, though this finding requires validation in larger cohorts with higher-frequency SPI sampling, offering a new direction for SPI interpretation. Large-scale, multicenter trials are warranted to validate the predictive utility of Tb-TW-SPI. Clinical Trial Registration, China Clinical Trial Registry: ChiCTR2400088444.

Humans↗

PubMed2026

Non-destructive prediction of lead content in oilseed rape leaves by fluorescence hyperspectral technology based on neural network.

Based on fluorescence hyperspectral imaging (FHSI), this study targeted rapid, non-destructive quantification of lead (Pb) content in oilseed rape leaves treated with varying silicon (Si) concentrations, acquiring fluorescence spectra over the 484.43-1001.61 nm wavelength range. To optimize spectral data quality, preprocessing methods (Savitzky-Golay smoothing, first derivative, detrending) were comprehensively compared. Characteristic wavelengths were then selected via interval variable iterative shrinkage, which effectively compressed data dimensionality and reduced computational load. A hybrid SE-CL1DA model, fusing a 1D convolutional neural network, a long short-term memory network and SE attention mechanism was constructed, with Bayesian optimization tuning hyperparameters to boost stability. The BO-SE-CL1DA outperformed both traditional machine learning and insufficiently optimized deep learning model (Rp2=0.9609, RMSE = 0.0377 mg/kg, RPD = 5.1736), thus enabling accurate Pb estimation, supporting Si-regulated heavy metal stress management and facilitating agricultural contamination monitoring.

Plant Leaves↗

PubMed2026

Artificial intelligence for anticancer drug discovery from natural products of macroalgae and sponges: A systematic review.

Marine natural products (MNPs) from macroalgae and marine sponges have inspired clinically important anticancer agents, including the cytarabine pharmacophore and the eribulin scaffold, while cyanobacterial dolastatin chemistry supplies the auristatin payloads of several marine-inspired antibody-drug conjugates (ADCs) such as brentuximab vedotin. Artificial intelligence (AI) methods, encompassing both classical machine learning (ML) with hand-engineered features and modern deep learning (DL) with many-layered neural networks, are increasingly supporting key decisions in natural-product anticancer drug discovery, including bioactivity prediction, target identification, absorption, distribution, metabolism, excretion and toxicity (ADMET) filtering, generative analogue design, and the selection of preclinical candidates. DL architectures relevant to this field include graph neural networks, transformer-based molecular generators, diffusion models for protein-ligand docking, and convolutional networks for mass spectrometry, while classical ML contributes interpretable fingerprint-based bioactivity models and molecular networking for dereplication. This review follows a systematic literature review methodology to organize the landscape of AI methods now applied to MNP anticancer discovery, distinguishing ML and DL approaches where relevant, situating them within the chemical context of macroalgal and sponge-derived oncology leads, and critically examining published case studies, including validation level (computational, in vitro, in vivo, clinical). The principal bottleneck for medical translation has shifted partly from algorithmic capability toward data infrastructure and experimental validation. Sparse, heterogeneous, and taxonomically biased bioactivity records limit what current models can learn and reduce the reliability of AI-prioritized candidates entering the preclinical pipeline. A roadmap is proposed that prioritizes open MNP-specific benchmarks, symbiont-aware modeling, and active learning loops with synthesizability and ADMET constraints. These AI workflows may accelerate the prioritization of marine-derived anticancer leads and support earlier, more evidence-based translational decisions in oncology drug development.

Biological Products↗

PubMed2026

Plasma proteome profiling identifies XPNPEP3 as a novel biomarker associated with metabolic dysfunction-associated steatotic liver disease in patients with type 2 diabetes mellitus.

OBJECTIVE: To identify plasma protein differences between type 2 diabetes mellitus (T2DM) patients with and without metabolic dysfunction-associated steatotic liver disease (MASLD), and to evaluate the diagnostic potential of X-prolyl aminopeptidase 3 (XPNPEP3) for identifying MASLD in T2DM patients. METHODS: Twenty T2DM inpatients were categorized into groups with and without MASLD and their plasma samples were analyzed using data-independent acquisition mass spectrometry, followed by bioinformatics analysis to identify differentially expressed proteins. The cohort was then expanded to 84 patients, and plasma XPNPEP3 levels were validated by enzyme-linked immunosorbent assay. Correlation between XPNPEP3 and clinical indicators were evaluated, and diagnostic performance was determined via receiver operating characteristic (ROC) analysis. Immunohistochemistry was employed to compare hepatic XPNPEP3 expression between the two groups. RESULTS: Proteomic analysis identified 176 differentially expressed proteins, with XPNPEP3 exhibiting the most significant down-regulation by fold change. In the validation cohort, plasma XPNPEP3 was significantly lower in T2DM+MASLD versus T2DM alone. XPNPEP3 levels were negatively correlated with diabetes duration, liver function markers, and triglyceride levels, and was identified as an independent factor inversely associated with MASLD in T2DM.ROC analysis demonstrated strong diagnostic performance for XPNPEP3, further enhanced when combined with BMI and diabetes duration.  Immunohistochemistry confirmed reduced hepatic XPNPEP3 expression in T2DM+MASLD patients. CONCLUSIONS: Lower plasma XPNPEP3 is independently associated with MASLD in T2DM patients and demonstrates strong diagnostic potential, positioning XPNPEP3 as a promising biomarker for diagnosing MASLD in T2DM patients and a novel target for non-invasive diagnostic tool development.

Humans↗

PubMed2026

Analysis of end-stage renal disease mediated by cuproptosis-related genes.

OBJECTIVE: The complex pathophysiological mechanism of end-stage renal disease (ESRD) has not been fully understood. Cuproptosis is a newly discovered type of programmed cell death. Therefore, this study attempts to clarify the relationship between cuproptosis-related genes (CRGs) and the phenotype of ESRD. MATERIALS AND METHODS: The National Center for Biological Information Gene Expression Omnibus database was applied to obtain the GSE37171 dataset comprising whole-genome microarray analysis of peripheral blood samples. A 3 : 1 case-control design was employed with 75 ESRD patients and 20 healthy controls who were frequency-matched for age, sex, and ethnicity. Based on differentially expressed genes (DEGs) and genes related to cuproptosis, CRGs were identified. Thereafter, we explored two different subpopulations based on the cuproptosis gene and analyzed their expression and immune infiltration. Genes specific to the CRG cluster were identified through the weighted gene co-expression network analysis algorithm, and the best prediction model was determined and verified by four machine learning methods. RESULTS: The study identified 14 differentially expressed CRGs, among which ATP7B, SLC31A1, LIAS, LIPT1, DLD, MTF1, CDKN2A, DBT, and DLST had relatively high expression levels in the ESRD samples. Compared with the control group, expression levels of FDX1, DLAT, PDHA1, PDHB, and GLS were significantly lower in the ESRD group, and CRGs played a key role in the regulation of immune infiltration in ESRD. Two cuproptosis-related molecular clusters were identified in the ESRD samples. Cluster2 was more correlated with the immune infiltration of ESRD. By analyzing the intersection points between CRG cluster and key genes of ESRD, a total of 888 specific DEGs were identified. Functional differences related to specific DEGs were further explored using gene set variation analysis. Five significant genes (SMC5, USP47, USP53, AGA, and DMXL1) were identified by the support vector machine model as key predictors for ESRD disease risk, achieving an area under the curve (AUC) of 1.00 in internal validation. However, external validation in independent cohorts is required prior to clinical application. Individual gene analysis showed an AUC > 0.81 in discriminating ESRD patients from healthy controls, and the expression of all 5 genes in ESRD patients was significantly lower than in the control group. CONCLUSION: This study clarified the relationship between CRGs and the phenotype of ESRD, analyzed their specific roles in the immune microenvironment, and obtained a predictive model, providing new insights for the study of its potential therapeutic targets.

Humans↗

PubMed2026

Development and Validation of a Predictive Model for Identification of Cognitive Impairment Risk in Older Adults with Subjective Cognitive Decline：A Longitudinal Study.

BACKGROUND: Subjective cognitive decline (SCD) is a transitional state between objective cognitive impairment and cognitively intact mental status, providing a critical window for implementing preventive interventions to delay objective cognitive decline. AIMS: We aimed to develop a predictive model for SCD progression in older adults with mild cognitive impairment (MCI). This model will facilitate the identification of risk factors and establishment of targeted interventions for community-based SCD management. METHODS: Data from the China Health and Retirement Longitudinal Study (CHARLS) was utilized in this study, extracting 18 indicators. Potential predictors selected through univariate Cox regression and LASSO regression analyses were sequentially incorporated into a multivariable Cox regression model. A nomogram was constructed to establish a predictive model. Model validation encompassed Area Under Curve (AUC) metrics for discriminative capacity, complemented by quantitative assessments using calibration curve analysis for precision verification and decision curve analysis (DCA) for clinical utility evaluation. RESULTS: A total of 1099 older adults with SCD were included in the final analysis, of whom 114 (10.3%) developed MCI. Multivariable Cox regression identified residence, marital status, educational level, social participation, gait speed, and baseline cognitive function. The model demonstrated time-dependent AUC values of 0.885, 0.830, 0.839, and 0.836 in the training set when evaluating discriminative capacity at 2-, 4-, 7-, and 9-year, respectively. The predictive model showed excellent predictive ability according to AUC, calibration curve, and DCA. CONCLUSIONS: A predictive model was created to estimate the risk of developing MCI in older individuals with SCD, offering clinician-actionable intervention benchmarks for preventive care.

Humans↗

PubMed2026

The role of artificial intelligence in the diagnosis and prognosis of traumatic brain injury based on brain CT scans: a systematic review.

Traumatic brain injury (TBI) is a leading cause of emergency department visits and a major contributor to injury-related mortality and long-term neurological disability. Non-contrast computed tomography (CT) is the gold-standard imaging modality for the rapid diagnosis of TBI. Clinical outcomes depend strongly on early detection and prompt acute management. Artificial intelligence (AI)-based models may support faster automated identification of traumatic findings and early prediction of patient prognosis. A systematic literature search was conducted in PubMed/MEDLINE, Scopus, IEEE Xplore, ACM Digital Library, and the Cochrane Library in accordance with PRISMA 2020 guidelines to evaluate AI-based models for automated detection of TBI-related findings on CT and for prediction of clinical outcomes. Risk of bias and applicability were assessed using QUADAS-2 for diagnostic accuracy studies and PROBAST + AI for prediction model studies. Twenty-two studies were included. Sixteen studies evaluated diagnostic tasks and 10 evaluated prognostic outcomes, with four studies contributing to both categories. Diagnostic performance was generally high, with many studies reporting AUC values approaching or exceeding 0.90, particularly for larger lesion volumes.Prognostic performance was more variable, with moderate to high discrimination and substantial heterogeneity. Only 9 studies incorporated independent external validation, and performance was frequently lower in external cohorts. All prognostic model studies were judged to be at high overall risk of bias using PROBAST + AI, and most diagnostic accuracy studies also demonstrated high or unclear risk of bias in at least one QUADAS-2 domain, most frequently in patient selection. AI-based models applied to brain CT demonstrate strong technical performance for both diagnostic and prognostic tasks in TBI. However, most studies relied on retrospective designs and lacked independent external validation which limits models generalizability and raises concern for potential overfitting. Prospective, multicenter studies with standardized methodologies and rigorous external validation are required before widespread clinical implementation.

Humans↗

PubMed2026

Assay-dependent variability in peptide biomarker quantification: experimental evidence from renalase in chronic kidney disease.

BACKGROUND: Renalase is a promising biomarker for kidney disease, but published levels vary widely between studies. We hypothesised that variability in commercial enzyme-linked immunosorbent assays (ELISAs) kits and matrix effects (serum vs plasma) drive these inconsistencies. METHODS: Paired serum and plasma samples from 56 participants (28 chronic kidney disease (CKD) stages 2-5, 28 healthy controls) were tested using three commercial renalase ELISAs (BTLAB, Cloud-Clone, EIAab). We assessed intra-assay precision, inter-assay agreement (Spearman's rank correlation and Bland-Altman analysis on log10-transformed values), matrix effects, and associations with estimated glomerular filtration rate (eGFR). Diagnostic performance was evaluated by Receiver operating characteristic (ROC) analysis. RESULTS: Inter-assay renalase concentrations differed markedly (up to orders of magnitude), with weak inter-assay correlations (r ≤ 0.25). Bland-Altman analyses revealed large, systematic biases between kits. Only the BTLAB assay showed consistent serum/plasma agreement, a significant correlation with eGFR (ρ ≈ 0.32-0.42, p < 0.05), and moderate discriminatory performance for CKD in serum (AUC = 0.70) and plasma (AUC = 0.68). Cloud-Clone and EIAab produced divergent results and strong matrix-dependent biases. CONCLUSIONS: Observed variability among commercial ELISA platforms may compromise comparability between studies. Harmonisation, standardised reference materials, and cross-validation are necessary before renalase assays can be used reliably in clinical practice.

Humans↗

PubMed2026

Pharmacokinetic and Pharmacodynamic Bio-Similarity of ADL-018 to Innovator Omalizumab: A Randomized Study in Healthy Adults.

Bioequivalence and safety of ADL-018, an omalizumab biosimilar, were compared with United States-licensed omalizumab (US-OMA) and European Union-approved omalizumab (EU-OMA), both approved for allergies. Healthy adults were randomized (1:1:1) to receive a dose of ADL-018, US-OMA, or EU-OMA (150 mg/mL). Pharmacokinetic (PK) parameters, including AUC(0-last), AUC(0-∞), and Cmax, were considered equivalent if 90% CIs of geometric mean ratios (GMRs) were within predefined equivalence margin (0.80-1.25) using ANCOVA model. Other PK parameters, pharmacodynamics (PD) (free/total immunoglobulin E [IgE]), immunogenicity, and safety were compared. Overall, 306 participants (n = 102 per arm) were dosed; 287 completed the study. Equivalence of primary PK parameters was confirmed for pairwise comparisons, with 90% CIs within the predefined margin (GMRs of ADL-018 vs US-OMA: AUC(0-last)-1.08, AUC(0-∞)-1.07, Cmax-1.05; GMRs of ADL-018 vs EU-OMA: AUC(0-last)-1.06, AUC(0-∞)-1.06, Cmax - 1.05; and GMRs of US-OMA vs EU-OMA: AUC(0-last)-0.99, AUC(0-∞)-0.99, Cmax-1.00). PK/PD parameters were comparable across arms. Increase in total IgE (AUEC ∼30,000 to 35,000 h IU/mL) and decrease in free IgE (AUEC ∼29,000 to 35,000 h IU/mL) were comparable across arms. Similar incidence of adverse events across arms (treatment-emergent adverse events: ADL-018, n = 6; US-OMA, n = 5; EU-OMA, n = 4) was observed. ADL-018 demonstrated PK/PD equivalence and comparable safety profile to reference omalizumab.

Humans↗

PubMed2026

Pharmacokinetics and safety of TBAJ-587, a novel antimycobacterial diarylquinoline, in healthy participants.

TBAJ-587 is a second-generation diarylquinoline with greater antimycobacterial activity and a potentially better safety profile than the first-generation bedaquiline. It is currently under development for the treatment of drug-susceptible and drug-resistant tuberculosis. A first-in-human trial of TBAJ-587, including single and multiple ascending oral doses and a dedicated food-effect cohort, was conducted in 92 healthy adults. Plasma exposures of TBAJ-587 were generally linear for AUCtau and slightly subproportional for Cmax, with the major circulating active metabolite, M3, remaining low relative to the parent. A high-fat meal increased the mean Cmax and AUClast 3.46- and 2.26-fold, respectively. TBAJ-587 accumulated with multiple dosing over the 28-day period, with mean accumulation ratios across the three tested doses ranging from 1.69 to 2.32 for Cmax and from 2.74 to 3.73 for AUCtau. However, steady-state conditions were not yet reached on day 28. Mean terminal half-lives after 28-day dosing of TBAJ-587 ranged from approximately 80 to 111 days. There were no deaths or serious adverse events, and TBAJ-587 was generally safe and well tolerated at single doses of 25-800 mg under fasting conditions and multiple doses of 50-200 mg once daily for 28 days after a standard breakfast. In addition, no dose- or time-dependent effects were noted for any of the other safety and tolerability parameters, including no clinically significant effects on the QTc interval. These results support further investigation of TBAJ-587 for the treatment of tuberculosis.CLINICAL TRIALSThis study is registered with ClinicalTrials.gov as NCT04890535.

Humans↗

PubMed2026

One Year After a Cyberattack: Lessons Learned and Dosimetric Analysis of Contingency Radiotherapy Plans.

PURPOSE: Cyberattacks on health care institutions pose significant risks to patient care, particularly in radiotherapy departments, which are heavily reliant on digital systems. This study examines the impact of a ransomware attack on our hospital and evaluates the effectiveness of the contingency measures implemented to resume radiotherapy treatments. METHODS AND MATERIALS: Following the cyberattack, our radiotherapy department faced a complete shutdown. After an initial estimate considering a shutdown of several weeks, a contingency plan was executed, including manual patient data retrieval and collaboration with a backup hospital. Contingency plans were prepared and delivered within hours, despite a partial lack of information. These plans allowed some patients to restart treatment 3 days after the attack. A dosimetric analysis was performed for the contingency plans, including various pathologies, mainly glioblastoma, head and neck cancers, and lung cancer. We compared the original and contingency plans in terms of dose coverage to the clinical target volume, biological effective dose, and their clinical impact as assessed at the 1‑year follow‑up after the cyberattack. RESULTS: Treatments resumed within 12 days at our hospital. Patients with glioblastoma showed good target coverage because of generous margins, resulting in favorable outcomes. In head and neck cases, the lack of detailed imaging led to significant target volume misses, suggesting that more conservative initial treatments could have been beneficial. Lung cases demonstrated accurate peripheral lesion targeting but faced challenges in central lesions because of the absence of positron emission tomography information. In most cases, the approach of using a contingency plan, even with limited information, led to a higher biological effective dose than would have been achieved if treatment had been stopped until full recovery at our hospital. CONCLUSIONS: The study highlights the critical importance of robust contingency planning in radiotherapy departments, emphasizing the need for backup systems and tailored approaches based on tumor location and available diagnostic information. These lessons emphasize that preparedness for digital disruptions should not focus exclusively on information and technology infrastructure.

Humans↗

PubMed2026

CT-Derived pelvic morphometry for preoperative risk assessment of recurrent unilateral inguinal hernia.

BACKGROUND: Recurrent inguinal hernia remains a significant challenge in abdominal wall surgery despite advances in mesh-based repair techniques and minimally invasive approaches. Although pelvic skeletal morphology has been implicated in inguinal hernia development, its association with recurrent disease remains incompletely understood. This study aimed to evaluate computed tomography (CT)-derived pelvic morphometric parameters and investigate their potential value in preoperative recurrence risk assessment. METHODS: This retrospective study included 251 male patients with preoperative abdominal CT examinations and complete clinical records who underwent elective inguinal hernia repair at a tertiary referral center. After applying the predefined eligibility criteria, 188 patients with unilateral inguinal hernias constituted the primary study cohort, including 162 primary and 26 recurrent unilateral hernias. The Radoievitch angle and Ami's line were measured independently by two blinded radiology residents using a standardized CT-based pelvic morphometric measurement protocol, and the mean values were used for analysis. Multivariable logistic regression and receiver operating characteristic (ROC) curve analyses were performed to evaluate the association between pelvic morphometric parameters and recurrent inguinal hernia. RESULTS: Patients with recurrent unilateral inguinal hernias demonstrated significantly greater affected-side Ami's line measurements (8.27 ± 0.63 vs. 7.90 ± 0.71 cm, p = 0.014) and larger Radoievitch angles (40.68 ± 4.02° vs. 38.80 ± 3.68°, p = 0.018) than patients with primary unilateral hernias. Both the Radoievitch angle (OR 1.14, 95% CI 1.01-1.28, p = 0.033) and Ami's line (OR 2.26, 95% CI 1.14-4.49, p = 0.020) remained independently associated with recurrent inguinal hernia after adjustment for age and body mass index. ROC analysis demonstrated modest discriminatory performance (AUC 0.634 for the Radoievitch angle and 0.633 for Ami's line), while the multivariable model incorporating age, body mass index, and Ami's line showed slightly improved discrimination (AUC 0.655). CONCLUSION: CT-derived pelvic morphometric parameters were independently associated with recurrent unilateral inguinal hernia. Although their individual discriminatory performance was modest, standardized CT-based pelvimetry may serve as an objective adjunctive tool for individualized preoperative recurrence risk assessment in patients who already undergo CT imaging for unrelated clinical indications. Prospective multicenter studies are warranted to validate these findings and determine their clinical applicability.

Humans↗

PubMed2026

Unveiling the power of TIIC: A prognostic tool for esophageal adenocarcinoma.

BACKGROUND: Esophageal adenocarcinoma (EAC) remains a lethal malignancy with limited prognostic tools for guiding immunotherapy. Tumor-infiltrating immune cells (TIICs) play a critical role in EAC prognosis and treatment response. METHODS: We integrated single-cell RNA sequencing and bulk transcriptome data from TCGA and GEO databases. TIIC-specific RNAs were identified via tissue specificity index calculation combined with machine learning feature selection. Twenty machine learning algorithms were benchmarked to construct an optimal TIIC signature score (TIIC-Score) based on the comprehensive C-index. Immunotherapy response, genomic mutation, and copy number variation were analyzed. Summary-data-based Mendelian randomization (SMR) and two-sample Mendelian randomization (MR) were performed to explore genetic associations. Core prognostic TIIC-related genes were functionally validated in esophageal cancer cell lines through loss-of-function assays. RESULTS: The TIIC-Score demonstrated robust prognostic value for 1-, 2-, and 3-year overall survival across multiple cohorts, outperforming 22 published models. High TIIC-Score was associated with poor survival and increased chromosomal instability. Mutation profiling revealed high frequencies of TP53 (78.2%), TTN (48.7%), and SYNE1 (30.8%). MR analysis identified a significant association between gastro-oesophageal reflux and EAC risk at SNP rs8130507. Functionally, CCNI was upregulated in esophageal cancer cells, and its knockdown suppressed malignant phenotypes while promoting apoptosis, supporting its pro-tumorigenic role. CONCLUSION: The TIIC-Score provides a novel prognostic framework for EAC that effectively stratifies patient risk and may help identify individuals most likely to benefit from immunotherapy.

Esophageal adenocarcinoma↗