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PubMed2026

Pharmacokinetic Differences Between Fast-Acting, Standard, and Placebo Cannabis Edibles.

INTRODUCTION: Edibles have become the second-most used cannabis product in legal U.S. states, wherein 64% of cannabis consumers reported using edibles within the past year. Among expansions to the legal cannabis industry are the newly marketed "fast-acting" edible compounds, which may address many of the issues associated with edible use related to overdose and dose management. The study hypotheses were that fast-acting edibles would reach peak concentration significantly faster than standard edibles and placebo edibles. MATERIALS AND METHODS: Twenty participants completed three arms within-subjects designed study to test hypotheses. The three arms were ingestion of a (1) fast-acting edible, (2) a standard edible, and (3) a Δ9-tetrahydrocannabinol (THC) terpene-derived placebo edible that was indistinguishable from the two THC-containing edibles. Blood plasma was analyzed for the presence of THC and THC analytes. The pharmacokinetic parameters tested were time to max concentration (Tmax), maximum concentration (Cmax), terminal half-life (t1/2), and area under the curve (AUC). RESULTS: Results supported study hypotheses in that Tmax was significantly faster for the fast-acting edible, observed 30 min post-ingestion and, on average, 30 min earlier than the Tmax for the standard edible. There were no significant differences between the fast-acting and standard edibles on Cmax, t1/2, and AUC; however, both the fast-acting and standard edibles were significantly different compared with the placebo across all pharmacokinetic parameters. DISCUSSION: The results indicate that the microencapsulation technology used to create the fast-acting edible enabled analyte concentrations to peak significantly faster compared to the standard and placebo edibles.

Humans↗

PubMed2026

Structured robotic colorectal training in a non-tertiary NHS hospital: a 502-case consecutive cohort implementation study.

Robotic-assisted colorectal surgery has expanded rapidly across NHS practice in the UK. Structured unit-wide training pathways are essential for safe technology adoption, yet published outcome data from non-tertiary hospitals remain limited. This study describes the implementation and feasibility of a unit-wide robotic colorectal program at a high-volume non-tertiary hospital, reporting outcomes across 502 consecutive resections performed by eight consultant surgeons and presenting these in the context of nationally published benchmarks. A retrospective cohort study of 502 consecutive robotic colorectal resections performed at York Teaching Hospital between May 2022 and December 2025. Eight consultant surgeons (A-H) participated in a structured four-phase training pathway incorporating simulation training, proctored cases, complexity-based case progression, and formal credentialing. Primary outcomes were 30-day mortality, unplanned return to theatre (RTT), and anastomotic leak (AL). Anastomotic leak was calculated using only patients who underwent anastomosis as the denominator. Procedure-stratified and individual surgeon outcomes with 95% confidence intervals were reported. Risk-adjusted cumulative sum (RA-CUSUM) analysis was performed to evaluate learning curves. Outcomes are presented descriptively alongside nationally published reference data; no formal statistical comparison against national benchmarks was performed. 502 robotic colorectal resections were performed. Mean patient age was 70.0 ± 11.3 years; 58.4% were male. Median ASA grade was III. The indication was malignancy in 89.2% of cases. Length of stay was non-normally distributed and is therefore reported using median and interquartile range in the revised analysis. Key outcomes: - 30-day mortality: 1.0% (5/502; 95% CI 0.4-2.3%) - Unplanned return to theatre (RTT): 5.2% (26/502; 95% CI 3.6-7.5%) - Anastomotic leak (AL): 3.3% (15/450; 95% CI 2.0-5.5%; denominator = patients with anastomosis) - 30-day unplanned readmission: 5.0% (25/502; 95% CI 3.4-7.2%) - Conversion to open surgery: 3.6% (18/502; 95% CI 2.3-5.6%) - Lymph node yield ≥12: 91.3% of cancer resections - R0 resection rate: 95.1% of cancer resections All primary outcomes fell within or below the published reference ranges used for descriptive context. RA-CUSUM trajectories were heterogeneous: no surgeon crossed the predefined upper control limit, but several curves showed later upward movement. Accordingly, the analysis is interpreted as safety surveillance rather than evidence of uniform performance improvement. RA-CUSUM monitoring showed that no surgeon crossed the predefined upper control limit; however, heterogeneous trajectories precluded a claim of uniform performance improvement.

Humans↗

PubMed2026

Long-Term Weight Trajectories in Infants Receiving Prolonged Breastfeeding: Impact of Formula Supplementation.

OBJECTIVE: This study compared weight trajectories in infants breastfed for at least 1 year, with or without formula supplementation. STUDY DESIGN: A retrospective cohort of 252 infants followed in well-baby clinics. All infants were breastfed ≥12 months and received complementary foods at 4-6 months. Of these, 174 received no formula, and 78 received daily formula supplementation. Anthropometric data were collected from birth to 5 years of age. RESULTS: Among males, those breastfed without formula had significantly lower body weight between 13 and 18 months compared with those receiving formula (10.20 kg vs. 10.97 kg, p < 0.001). From 19 months onward, no significant weight differences were observed and trajectories converged through 5 years. CONCLUSION: Prolonged breastfeeding in male infants is associated with lower weight in early toddlerhood; however, differences resolve by age two. These findings suggest early variations may represent physiological patterns. Further controlled studies are needed.

Humans↗

PubMed2026

Phase 1 Study Evaluating Gefurulimab Pharmacokinetics and Safety Following Delivery Via Autoinjector or Prefilled Syringe With Needle Safety Device in Healthy Adults.

PURPOSE: Gefurulimab, a novel dual-binding nanobody targeting complement component 5 (C5), is in clinical development for anti-acetylcholine receptor antibody-positive generalized myasthenia gravis. Gefurulimab has a low molecular weight, enabling subcutaneous (SC) self-administration by autoinjector (AI) or prefilled syringe with needle safety device (PFS-SD). We compared gefurulimab pharmacokinetic (PK) exposure and safety in healthy adults following a single SC dose administered by AI versus PFS-SD. METHODS: In this phase 1, open-label, randomized, parallel-group study (NCT06208488), healthy participants aged 18 to 65 years were stratified by weight and randomized equally to 1 of 6 combination groups of device and injection site (abdomen/thigh/upper arm). Participants received a single SC dose of gefurulimab on day 1 and were assessed throughout the 92-day evaluation period. Primary endpoints were PK parameters for each device: maximum observed concentration (Cmax) and area under the serum concentration-time curve (AUCinf, AUClast). PK across injection sites, pharmacodynamics, safety, immunogenicity, and device performance were also assessed. FINDINGS: Overall, 175 participants were randomized: AI (n = 87), PFS-SD (n = 88). Geometric least squares mean ratios (90% CI) comparing AI/PFS-SD for Cmax, AUCinf, and AUClast were 97.6% (94.5-100.8), 99.6% (96.1-103.3), and 98.8% (95.2‒102.6), respectively. Secondary analyses found no meaningful differences in PK parameters across injection sites. Serum-free C5 concentrations over time, treatment-emergent adverse event (TEAE) profiles, and antidrug antibody responses were similar between cohorts. Most TEAEs were mild; none led to study discontinuation. IMPLICATIONS: SC administration of gefurulimab by AI and PFS-SD was well tolerated with comparable exposure, meeting bioequivalence criteria.

Humans↗

PubMed2026

Plasma proteomics reveal SERPINA1 and CD59 as candidate biomarkers for COVID-19 severity stratification and prognosis prediction.

BACKGROUND: COVID-19 has been closely associated with coagulation abnormalities. However, existing biomarkers, including D-dimer and fibrin degradation products (FDP), exhibit limited accuracy in stratifying disease severity and predicting long-term clinical outcomes. OBJECTIVES: This study aimed to use proteomic analysis to identify plasma biomarkers associated with COVID-19 severity and prognosis, and validate their predictive utility for mortality and thromboembolic complications. METHODS: Plasma proteomic profiles were analyzed across three COVID-19 severity classes. Differential expression analysis and functional analysis were performed. Clustering analysis was used to identify proteins correlated with disease severity. Candidate biomarkers were validated in an independent cohort. Predictive performance of the biomarkers for mortality, sepsis and venous thromboembolism was evaluated using bootstrap-corrected ROC analyses and multivariable regression analyses. RESULTS: Proteomic analysis revealed progressive involvement of the coagulation and complement pathway with increasing disease severity. SERPINA1 and CD59 were identified as candidate biomarkers and exhibited significantly higher plasma levels in severe cases. Bootstrap-corrected ROC analyses demonstrated strong predictive performance: SERPINA1 achieved AUCs of 0.775 and 0.924 for 30-day and 12-month mortality, and CD59 achieved AUCs of 0.720 for sepsis; the combined model further improved prediction of 12-month mortality (AUC 0.946) and sepsis (AUC 0.904), outperforming D-dimer and FDP. Multivariable regression confirmed their independent prognostic value. CONCLUSION: This exploratory study identifies SERPINA1 and CD59 as candidate prognostic biomarkers in COVID-19, highlighting the role of coagulation and complement-related pathways in disease severity and warranting further prospective validation.

Humans↗

PubMed2026

Prognostic value of early changes in the frontal QRS-T angle in patients with heart failure and left bundle branch block undergoing cardiac resynchronization therapy.

BACKGROUND: Cardiac resynchronization therapy (CRT) reduces morbidity and mortality in selected patients with heart failure (HF). The frontal QRS-T angle (FQTA), reflecting ventricular depolarization-repolarization heterogeneity, has been associated with major adverse cardiovascular events (MACE). We aimed to assess the prognostic value of changes in the FQTA after CRT in predicting long-term MACE. METHODS: A total of 223 consecutive HF patients with left bundle branch block who underwent CRT between 2018 and 2022 were retrospectively analyzed. The FQTA was measured before and after CRT, and the change (ΔFQTA) was calculated. Receiver operating characteristic (ROC) analysis was performed to determine the optimal cutoff value for predicting the primary outcome, MACE. Patients were subsequently stratified according to this cutoff value. Independent predictors were identified using multivariable Cox proportional hazards regression analysis. RESULTS: ROC analysis identified 22.5° as the optimal cutoff value for predicting MACE (AUC: 0.711; 95% CI: 0.642-0.781; p < 0.001). During a mean follow-up of 34.6 ± 17.6 months, patients with ΔFQTA <22.5° had a significantly higher incidence of MACE compared with those with greater angle reduction (44.7% vs. 11.9%; p < 0.001). In multivariable Cox regression analysis, chronic kidney disease (HR: 2.517; p = 0.002) and ΔFQTA <22.5° (HR: 4.56; p < 0.001) were independently associated with MACE. CONCLUSION: A greater reduction in FQTA after CRT is associated with improved long-term outcomes and may serve as a practical electrocardiographic marker for risk stratification.

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

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

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

Comparative Bioavailability of Trimodal (CTx-1301) Versus Bimodal Dexmethylphenidate Modified-Release Formulations in Adults with Attention-Deficit/Hyperactivity Disorder: A Randomized, Single-Dose, Crossover Study.

BACKGROUND AND OBJECTIVES: Attention-deficit/hyperactivity disorder (ADHD) is a chronic neurodevelopmental disorder that often requires sustained symptom control throughout the day. Although bimodal extended-release dexmethylphenidate (d-MPH XR) formulations provide initial and intermediate drug release, they may not consistently maintain therapeutic exposure into the late afternoon and evening. Trimodal formulations with an additional delayed release component may extend drug exposure later in the day, although this remains to be established. To explore differences in pharmacokinetic (PK) profiles between trimodal (CTx-1301) and bimodal delivery of d-MPH XR, a comparative bioavailability study was conducted at the highest and lowest doses for both formulations. METHODS: In this randomized, 4-period, crossover study, adults with ADHD received single doses of CTx-1301 (50 mg and 6.25 mg) and d-MPH XR (40 mg and 5 mg). Comparative bioavailability was assessed through adjusted geometric mean ratios for exposure parameters (maximum observed plasma concentration [Cmax], area under plasma concentration-time curve to last measurable concentration [AUClast] and extrapolated to infinity [AUC0-inf]), with a prespecified bioequivalence range of 0.80 to 1.25. Secondary endpoints included partial AUCs and safety assessments. RESULTS: The study population (N = 45) was predominantly male (88.9%) and White (55.6%), with mean age of 29.6 ± 8.01 years. Adjusted geometric mean ratios comparing the primary exposure parameters (Cmax, AUClast, and AUC0-inf) for CTx-1301 versus d-MPH XR were within the bioequivalence range (0.80-1.25) at both the high and low doses. The CTx-1301-to-d-MPH XR partial AUC ratios were within the bioequivalence range from 0 to 9 hours post-dose. At later intervals (AUC9-12 and AUC12-16), adjusted geometric mean ratios exceeded the upper bioequivalence threshold, consistent with the expected contribution of the third medication release component. Dose proportionality was observed between the two CTx-1301 doses and two d-MPH XR formulations. CTx-1301 was generally well tolerated. The most commonly reported adverse events included tachycardia, insomnia, headache, nausea, and euphoric mood. The incidence of treatment-emergent adverse events was numerically lower with CTx-1301 than with d-MPH XR; however, no statistical analysis was performed. CONCLUSIONS: Key exposure parameters including Cmax, AUClast, and AUC0-inf for trimodal CTx-1301 were statistically bioequivalent to bimodal d-MPH XR. Interval‑specific PK analyses demonstrated higher exposure with CTx‑1301 during later post-dose intervals (9-16 h), consistent with the formulation's third release component. However, the clinical relevance of these PK differences requires further evaluation. CTx-1301 demonstrated dose proportionality and was well tolerated at high and low doses. REGISTRATION: ClinicalTrials.gov, NCT04138498; 19 September 2019.

Humans↗

PubMed2026

A Dynamic Nomogram to Predict Metabolic Dysfunction-Associated Fatty Liver Disease in Patients with Metabolic Syndrome.

BACKGROUND: Metabolic syndrome (MetS) involves multiple metabolic disorders. This study aimed to identify high-risk populations for metabolic dysfunction-associated fatty liver disease (MAFLD) in patients with MetS and to establish a dynamic predictive nomogram. METHODS: A total of 627 patients with MetS from six regions in Zhejiang Province were enrolled and categorized into MAFLD and non-MAFLD groups, then randomly assigned to training and validation sets at a ratio of 7:3. Independent predictors of MAFLD were identified using least absolute shrinkage and selection operator regression and multivariable logistic regression analyses. These predictors were then used to construct a dynamic nomogram. RESULTS: A total of 627 patients with MetS were included in the final analysis, of whom 77.0% (483/627) were diagnosed with MAFLD. Multivariable logistic regression analysis identified body mass index (BMI), waist circumference (WC), total cholesterol (TC), alanine aminotransferase (ALT), MetS-defined dysglycemia, and education level as independent risk factors for MAFLD. MetS-defined dysglycemia showed the highest odds ratio (OR) for MAFLD development [OR = 1.87, 95% confidence interval (CI): 1.07-3.29]. Although the number of MetS components and the metabolic syndrome score were significantly associated with MAFLD in univariate analysis, they were not independently associated with MAFLD in the multivariate model. A dynamic nomogram for predicting MAFLD risk in patients with MetS was developed and internally validated. The area under the receiver operating characteristic curve was 0.834 (95% CI: 0.787-0.880) in the training set and 0.839 (95% CI: 0.771-0.899) in the validation set, indicating strong predictive performance. Bootstrap internal validation demonstrated good agreement between predicted and observed outcomes in calibration curves. Decision curve analysis further indicated favorable clinical applicability of the nomogram. CONCLUSION: BMI, WC, TC, ALT, MetS-defined dysglycemia, and education level are independent risk factors for MAFLD. A dynamic nomogram for predicting MAFLD risk in patients with MetS was successfully developed and validated.

Humans↗

PubMed2026

Development and validation of a comprehensive prognostic model for 28-day ICU mortality in non-traumatic subarachnoid hemorrhage: an analysis based on the MIMIC-IV database.

BACKGROUND: Due to the complex pathophysiology of non-traumatic subarachnoid hemorrhage (SAH), accurate risk prediction remains a challenge. Our aim is to develop and validate a comprehensive prognostic model that integrates demographic characteristics, vital signs, laboratory parameters, and more, to provide clinical decision-making support in real-world practice. METHODS: We conducted a retrospective cohort study of 785 Non-traumatic subarachnoid hemorrhage patients. The cohort was randomly divided into a training set (n = 549) and a validation set (n = 236). Feature selection was performed using LASSO regression, followed by backward stepwise Cox regression for optimization. A nomogram was constructed based on independent predictive factors, and model performance was assessed using discrimination, calibration, and decision curve analysis. To prevent immortal-time bias, all predictors were anchored to a fixed early (first-24-hour) measurement window, treatment variables were modelled as binary indicators rather than cumulative exposures, and a five-model sensitivity analysis with baseline-severity adjustment was performed. RESULTS: The development of our model followed a systematic approach: first, 15 potential predictive factors were selected via LASSO regression, which were then refined to 12 independent predictors using backward stepwise Cox regression. The final predictive factors included: Ventilation, AHT, Nimodipine 60 mg, Age, SAPS.II, Input amount, Calcium total, Platelet count, White blood cells, Anion gap, pH, and Chloride. The integrated model demonstrated excellent predictive ability for 7-day, 14-day, and 21-day mortality in both the training set (AUC: 0.972, 0.934, 0.898) and the validation set (AUC: 0.968, 0.948, 0.911). Calibration curves and decision curve analysis confirmed the model's reliability and clinical utility across different time points. We constructed a nomogram for individualized risk prediction. Univariate Kaplan-Meier survival analysis demonstrated significant stratification of survival outcomes by each predictor, while restricted cubic spline analysis revealed non-linear relationships between continuous variables and mortality risk. Random survival forest analysis identified the top three predictive factors (Nimodipine 60 mg, Ventilation, AHT) and compared them with our full 12-variable model, confirming superior performance of the integrated model at all time points. At the 28-day primary endpoint, the model achieved a time-dependent AUC of 0.898 (training) and 0.904 (validation); after restricting predictors to the early baseline window, the leakage-controlled model retained good discrimination (validation C-index 0.803). CONCLUSIONS: Our ICU 28-day mortality prognosis model demonstrated robust performance in predicting ICU 28-day mortality in non-traumatic subarachnoid hemorrhage. The model, through the nomogram, provides individualized risk assessment, aiding clinical decision-making and patient stratification.

Humans↗

PubMed2026

Investigation of Fatty Acid Metabolism-Associated Molecular CPOX and the Underlying Mechanism in Follicular Lymphoma.

Dysregulated lipid metabolism is a key driver of follicular lymphoma (FL). This study aimed to explore the lipid metabolism-related genes (LMRGs) and clarify the underlying roles and mechanisms in FL. Bioinformatics methods, including differential analysis, WGCNA, machine learning, and Mendelian randomization, were utilized to select the LMRGs in FL. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were conducted to investigate the function of the key LMRG. Receiver operator characteristic (ROC) was used to evaluate the diagnostic value of the key gene CPOX. A pan-cancer analysis investigated CPOX's expression level and immune correlations. In vitro experiments using FL cell lines (WSU-FSCCL, DOHH2) validated CPOX expression, and CPOX knockdown in DOHH2 cells was used to assess its impact on viability, migration, invasion, and fatty acid metabolism. CPOX was confirmed to be a risk factor, significantly overexpressed in FL, and exhibited effective diagnostic ability in FL (AUC = 0.731). Functional analysis linked CPOX to mitochondrial function, oxidative phosphorylation, and heme metabolic process. Pan-cancer indicated the dysregulated CPOX across multiple cancers and closely correlation with immune characteristics. Experimentally, CPOX was higher in the more invasive DOHH2 cells; and CPOX knockdown suppressed FL progression and reduced lipid droplet formation, triglyceride, total cholesterol, and free fatty acid levels. In conclusion, this study fills the gap in understanding the significance of lipid metabolism-related molecules in FL, and innovatively proposes that CPOX is a risk factor for FL. Knockdown of CPOX inhibits the FL progression, which is regulated by fatty acid metabolism.

Lymphoma, Follicular↗

PubMed2026

Diagnostic performance of machine learning models versus established risk stratification for intracranial aneurysm rupture: a systematic review and bivariate meta-analysis.

BACKGROUND: Machine learning (ML) models have been proposed to improve the discrimination of intracranial aneurysm rupture status beyond established clinical risk stratification tools. However, reported performance is heterogeneous and the relative contribution of model architecture and feature dominance remains unclear. METHODS: We performed a Preferred Reporting Items for Systematic Reviews and Meta-Analyses-diagnostic test accuracy systematic review and diagnostic meta-analysis of studies evaluating ML models for intracranial aneurysm rupture discrimination. PubMed, Embase and CENTRAL were searched to February 2026. Sensitivity and specificity were pooled using a bivariate random-effects model, with summary receiver operating characteristic curves generated across training, internal testing and external validation datasets. Models were compared with regression-based approaches and Population, Hypertension, Age, Size of aneurysm, Earlier subarachnoid haemorrhage, Site of aneurysm (PHASES) scores. Subgroup and meta-regression analyses explored associations between algorithm family and feature domain. RESULTS: Sixty-two retrospective cohorts (29 709 patients 209 models) met the inclusion criteria. In training datasets, pooled sensitivity and specificity for ML were 0.81 (95% CI 0.75 to 0.85) and 0.83 (0.80-0.86), with an area under the curve (AUC) of 0.878, exceeding PHASES (AUC 0.667). In testing datasets, ML retained higher discrimination (AUC 0.837) than regression models (0.806) and PHASES (0.646). In external validation, sensitivity was preserved (0.82), but specificity declined (0.66). Deep learning demonstrated the highest AUCs (training and testing). Incorporation of haemodynamic or radiomic features improved pooled discrimination relative to morphology alone. Evidence of small-study effects and mostly unclear Prediction Model Risk Of Bias Assessment Tool ratings were observed. CONCLUSIONS: ML approaches demonstrate higher pooled discrimination for aneurysm rupture status than conventional risk scores in retrospective datasets, but reduced external validation specificity and heterogeneity limit confidence for clinical translation. Prospective, externally validated, calibrated models are required before integration into routine cerebrovascular risk stratification.

Humans↗

PubMed2026

Transvalvular Flow Rate is Associated With Mortality Rate and Lifetime Loss in Aortic Valve Stenosis: A Meta-Analysis of Reconstructed Time-to-Event Data.

Low-flow states are associated with adverse outcomes in aortic stenosis (AS), but the prognostic value of transvalvular flow rate (TFR) has not been consistently established across studies. This study is a systematic review and meta-analysis of reconstructed time-to-event data was performed in accordance with Preferred Reporting Items for Systematic Reviews and Meta-analyses. PubMed/MEDLINE, EMBASE, and Cochrane Library were searched for studies (published by November 14, 2025) comparing low versus normal TFR in AS. Data were collected from Kaplan-Meier curves. The primary endpoint was all-cause mortality. Survival was assessed using pooled Kaplan-Meier curves, Cox regression, flexible parametric survival models, and restricted mean survival time (RMST) analysis. A total of 9 studies including 6,494 patients were analyzed; 2,575 (39.7%) had low TFR. At 8 years of follow-up, estimated survival was 34.1% (95% confidence interval [CI] 24.7% to 47%) in the low-TFR group and 63% (95% CI 58.9% to 67.4%) in the normal-TFR group. Low TFR was associated with higher all-cause mortality (hazard ratio 1.59, 95% CI 1.45 to 1.74, p < 0.001). We observed a progressively greater hazard over time, with the hazard ratio approaching 1.9 by 8 years. At 8 years, RMST in the normal-TFR group was 7.37 years (95% CI 7.21 to 7.53 years) versus 5.07 years (95% CI 4.91 to 5.23 years) in the low-TFR group, representing a lifetime loss of 2.3 years in the low-TFR group (ΔRMST -2.30 years, 95% CI -2.53 to -2.07 years, p < 0.001). In patients with AS, low TFR is associated with significantly higher mortality and lifetime loss. These findings support TFR as a clinically meaningful marker for risk stratification in AS.

Aortic Valve Stenosis↗

PubMed2026

Beyond predictive performance: A systematic review and critical methodological appraisal of AI/ML and conventional modelling strategies in breast, colorectal, and pancreatic Cancer.

BACKGROUND: Predictive modelling for cancer risk, treatment-related complications, and survival is central to precision oncology. Conventional logistic regression (LR) and Cox proportional hazards (CoxPH) regression remain widely used but are limited when modelling nonlinear interactions, high-dimensional imaging features, and multimodal clinical-metabolic predictors. Artificial intelligence (AI) and machine learning (ML) methods offer expanded capability through automated feature extraction, ensemble learning, and flexible survival modelling, but the evidence on when AI/ML adds value over conventional models across cancer sites and predictive tasks remains fragmented. OBJECTIVE: To systematically evaluate the methodological performance, validation strategies, and translational limitations of AI/ML models compared with conventional statistical models in published predictive-modelling studies for breast, colorectal, or pancreatic cancer. METHODS: PubMed, Scopus, and Web of Science were searched for studies published between January 2019 and March 2025. Two reviewers independently conducted title-and-abstract screening, full-text eligibility assessment, and PROBAST risk-of-bias assessment. Sixty-five studies (n = 907,567 participants) were narratively synthesised by cancer site, predictive task, model family, comparator, validation strategy, predictor modality, and calibration or explainability reporting. RESULTS: The 65 studies comprised breast cancer (n = 35), colorectal cancer (n = 21), and pancreatic cancer (n = 9). AI/ML superiority over LR and CoxPH was task- and data-dependent. CNN- and U-Net-based models predominated in imaging and body-composition tasks, tree-based ensembles consistently outperformed LR for tabular perioperative complication prediction, and CoxPH remained competitive, and in the largest pancreatic risk study, superior to XGBoost (C-index 0.802 vs 0.723) in well-structured datasets. PROBAST analysis-domain risk was moderate in 54 of 65 studies (83%), driven by limited external validation, sparse calibration reporting (11/65), and few decision-curve analyses (7/65). CONCLUSION: AI/ML adds the most methodological value in imaging-derived feature extraction and nonlinear perioperative prediction, while conventional regression remains preferable in large, structured datasets with linear predictors. Clinical translation requires standardised body-composition definitions, external validation, calibration assessment, decision-curve analysis, and explainability, in line with TRIPOD+AI and CLAIM standards.

Humans↗