Citations

Background and Objectives: Obstructive sleep apnea (OSA) is a heterogeneous disease with diverse clinical presentations and high global prevalence. Obesity is a common comorbidity in OSA, but its relationship with symptom subtypes remains unclear. This study aimed to evaluate the association between OSA symptom subtypes and obesity in a clinical cohort. Methods: This observational study analyzed data from the Santiago Obstructive Sleep Apnea (SantOSA) prospective clinical cohort, including adults with OSA confirmed by home sleep apnea testing. Symptom subtypes were identified using latent class analysis. Associations between obesity and symptom subtypes were evaluated using multivariable regression models adjusted for age, sex, tobacco use, RDI, T90%, TST, hypertension, and diabetes. Statistical significance was set at p < 0.05. Results: A total of 1167 patients were included (943 men). Latent class analysis identified three symptom subtypes: non-sleepy, disturbed sleep, and excessive daytime sleepiness. Among obese patients, 30.7%, 50.0%, and 19.3% were classified into these subtypes, respectively. Obesity prevalence was 50.2%, and compared with non-obese OSA patients, obese patients showed a higher prevalence of severe OSA (46.1% vs. 26.9%), hypertension (54.4% vs. 34.9%), and diabetes (37.2% vs. 19.4%), as well as higher ESS scores and higher RDI and T90% values (all p < 0.01). In adjusted analyses, obesity remained independently associated with the excessive daytime sleepiness subtype after controlling for age, sex, tobacco use, RDI, T90%, TST, and comorbidities. Conclusions: Obesity is highly prevalent in OSA and is associated with specific symptom-defined phenotypes, particularly excessive daytime sleepiness and disturbed sleep. These findings support the relevance of considering symptom profiles alongside traditional severity metrics.

BACKGROUND: Unruptured intracranial aneurysms (UIAs) affect up to 5% of the population, and rupture can lead to devastating subarachnoid hemorrhage. GLP-1RAs (glucagon-like peptide-1 receptor agonists) have anti-inflammatory and vasculoprotective effects, but their impact on aneurysm stability and subarachnoid hemorrhage outcomes is unexplored. This study evaluated whether GLP-1RA use is associated with reduced rupture risk and attenuated severity in the event of a rupture. METHODS: We conducted a retrospective cohort study using the TriNetX US Collaborative Network (2016–2024). Cohort 1 included patients with newly diagnosed, untreated UIA; cohort 2 included those who suffered aneurysm rupture. GLP-1RA exposure was defined as use before or within 3 months of UIA diagnosis (cohort 1) or before rupture (cohort 2). The primary outcome was aneurysm rupture risk in cohort 1. Secondary outcomes include presentation severity, vasospasm, and mortality in cohort 2. Propensity score matching balanced demographics, comorbidities, and medications. RESULTS: After propensity score match, cohort 1 of patients with untreated UIA included 8088 GLP-1RA users and 8088 nonusers. GLP-1RA use was associated with significantly lower probabilities of aneurysm rupture at 1-year (0.65% versus 1.51%; P <0.001), 3-year (1.18% versus 1.85%; P <0.001), and 5-year (1.49% versus 2.10%; P <0.001) timepoints, representing a 48% decrease in hazards (hazard ratio, 0.52 [95% CI, 0.38–0.69]). Among patients who suffered aneurysm rupture (cohort 2, N=298 after propensity score match), prerupture GLP-1RA use (n=149) was associated with lower rates of intraparenchymal hemorrhage (17.4% versus 33.6%; P <0.001), intraventricular hemorrhage (10.1% versus 23.5%; P <0.001), and clinically significant vasospasm (10.7% versus 24.2%; P =0.002), as well as numerically lower rates of 30-day mortality (9.4% versus 14.1%; P =0.21). CONCLUSIONS: For patients with untreated UIA, GLP-1RA use was associated with a 48% reduction in aneurysm rupture risk and, among those who suffered aneurysm rupture, milder clinical and radiographic severity. These findings suggest possible neuroprotective and vascular stabilizing effects of GLP-1RAs for patients with UIA.

ABSTRACT HLA‐B*08:345Q differs from HLA‐B*08:238 by one nucleotide substitution, introducing a premature stop codon in exon 5.

Abstract Background Markerless tumor tracking (MTT) using single‐energy (SE) kilovoltage (kV) imaging has been proposed as a technique for lung tumor motion management. However, bony structures can obscure the tumor and make tracking challenging. Dual‐energy (DE) subtraction imaging can suppress the bone and therefore enhance tumor visibility, potentially improving tracking accuracy. Purpose To develop a machine learning model that predicts the reliability of MTT for SE and DE imaging using phantom and patient data. Methods Images were collected at high and low energy (120/60 kV) using fast‐kV switching on dynamic thorax phantom with 5–15 diameter targets and 20 lung cancer patients, including one patient with two tumor locations. DE images were then generated off‐line using weighted logarithmic subtraction. Separately, a template‐based tracking algorithm was used to track the tumor on both phantom and patient images. For phantom data, the ground truth (GT) was defined by the cos 4 waveform programmed into the motion controller. Since the GT was unknown for patient data, it was estimated using a Kalman filter and further refined using visual inspection. The tracking success rate (TSR) was subsequently calculated. Based on TSR, patients were stratified into three tracking groups: good, moderate, and poor. A logistic regression (LR) model was implemented with tracking features, including match score, peak to side‐peak ratio (PSR), x ‐ and y ‐velocity. To address the distribution differences between phantom and patient data, a domain indicator and interaction features were also incorporated. The model was trained on phantom and 17 patient data using leave‐one‐out cross‐validation, and its performance was evaluated on three held‐out patients, one from each tracking group. Separate LR models were developed for each imaging modality, resulting in SE‐LR and DE‐LR models. For each held‐out patient, the confusion matrix, sensitivity, and specificity were reported. Results For both SE‐LR and DE‐LR models, PSR has the largest coefficient value, indicating that it is the most influential factor when determining the reliability of tracking. At a specificity of 95%, the DE‐LR model achieved significantly higher sensitivity than the SE‐LR model (0.908 vs. 0.731, p < 0.01 for phantom data and 0.657 vs. 0.589, p < 0.01 for patient data). In the three held‐out patients, the DE‐LR model outperformed the SE‐LR model across all metrics. The largest gains were found in the patient with good tracking quality. Conclusions The proposed LR models successfully predicted the reliability of SE‐ and DE‐MTT. Across phantom and patient datasets, the DE‐LR model consistently demonstrated improved performance compared with the SE‐LR model, suggesting that DE imaging may be beneficial for MTT applications.

Background: The role of venous Doppler ultrasound in assessing venous congestion in the setting of post-lung transplantation surgery is unknown. We performed a feasibility study on the use of venous Doppler ultrasound in the immediate post-operative period after lung transplantation in a retrospective, single-center observational evaluation. Patients had at least two venous congestion Doppler ultrasound exams involving interrogation of IVC diameter, hepatic vein, portal vein and renal vein Doppler assessments. Results: A total of 9 patients were included in our study with a median age of 60 years, 66.7% being male, and a median BMI of 20.9. All patients had VExUS grade ≥1 on their first venous Doppler exam. All nine patients had a decrease in portal vein pulsatility from initial to final exams. Correlation between portal venous pulsatility and net fluid balance was not statistically significant R= 0.33 (P= 0.39). Other parameters, including VExUS grade, IVC diameter, HV and RV Doppler changes from initial to final ultrasound exams showed variable changes in the post-operative period. Conclusion: Venous congestion Doppler ultrasound assessments appear feasible in immediate postoperative lung transplant patients. A multitude of factors beyond fluid status likely affect waveforms.

Background Tacrolimus dosing after kidney transplantation is complex, highly individualized, and prone to variability, which can impact graft outcomes. While machine learning (ML) approaches have been used primarily to predict tacrolimus concentrations, large language models (LLMs) may enable protocol-constrained clinical decision support by generating dosing recommendations aligned with established treatment guidelines. Methods We developed TacroDose AI, a protocol-constrained LLM based on GPT-4, to generate tacrolimus dosing recommendations aligned with institutional guidelines. Using 300 structured simulated clinical scenarios, we evaluated protocol adherence, error patterns, and reproducibility of model-generated dosing recommendations. Following review of initial outputs, a refined prompt ( TacroAI 2.0 ) was implemented to strengthen protocol constraints and structured output verification, and performance was reassessed. Results TacroDose AI achieved 72.9% protocol adherence, with 27.1% protocol deviations, including 13.9% sentinel errors with potential clinical significance. Prompt refinement substantially improved performance: TacroAI 2.0 achieved 91.7% adherence, reduced sentinel errors to 1.0%, and improved reproducibility from 77.3 to 86.7% (all p &lt; 0.01). Although rounding discrepancies increased, these represented minor numerical differences without clinical relevance. Performance improvements were observed across subtherapeutic, therapeutic, and supratherapeutic tacrolimus scenarios. Conclusion Protocol-constrained LLMs can produce guideline-concordant tacrolimus dosing recommendations with high adherence and improved reliability in simulated clinical settings. These findings highlight a potential framework for integrating generative AI with rule-based clinical protocols to support safe medication management. With further validation using real-world data, such systems could be integrated into electronic health records to support clinician-supervised, protocol-based immunosuppressive dosing in transplant care.

Multiple sclerosis (MS) is an immune-mediated chronic inflammatory and degenerative disease of the central nervous system (CNS). Typically occurring in young and middle-aged individuals, untreated MS can have high rates of disability and recurrence, thereby imposing a significant burden on the patient, their family, and society. Many factors are implicated in the etiology of MS, with the relationship between Epstein-Barr Virus (EBV) infection and the development of MS being the subject of extensive research recently. When the human body experiences a decline in immune function, it may trigger reactivation of EBV, and this reactivation is also believed to increase the risk of onset or relapse of MS. Currently, the phenomenon of cross-reactivity resulting from molecular mimicry following EBV infection (including reactivation status) is theorized as a contributing etiology. EBV-mediated abnormalities in T cells and B cells also play a key role in the development of MS. However, the underlying mechanisms have not been thoroughly understood. Meanwhile, the limited availability of effective treatment options for MS, in particular MS progression, underscores the urgent need for novel therapeutic strategies. Here, we discuss the pathophysiological mechanisms underlying MS, specifically emphasizing the relationship between EBV infection and the disease pathology. Furthermore, we introduced relevant pharmacological targets in order to propose a broader range of therapeutic alternatives for individuals diagnosed with MS.

ABSTRACT Richter transformation of Chronic lymphocytic leukemia/small lymphocytic lymphoma (CLL/SLL) into classic Hodgkin lymphoma (CHL-RT) is rare and remains incompletely understood. Two histologic subtypes are recognized: type 1 (CLL/SLL with scattered Hodgkin/Reed-Sternberg (HRS) cells) and type 2 (HRS cells within a polymorphous inflammatory background). In this multi-institutional study of 77 patients with CHL-RT (27 type 1 and 50 type 2), we characterized immune evasion markers, PD-L1 / PD-L2 copy number alterations, tumor microenvironment, and performed targeted next-generation sequencing on 37 CLL/SLL samples. HRS cells in CHL-RT displayed immune-evasion phenotypes similar to de novo CHL, though PD-L1 expression was lower in type 1 cases. PD-L1/PD-L2 gain/polysomy were frequent (83.3%). CLL/SLL with CHL-RT harbored increased mutations in XPO1, FBXW7, BIRC3, TRAF3 , and HLA-A versus reference CLL/SLL. Similar mutational profiles, demographics, and survival outcomes support a biological continuum between type 1 and type 2 CHL-RT, with distinct genetic features in CLL/SLL predisposing to CHL transformation. STATEMENT OF SIGNIFICANCE Classic Hodgkin lymphoma-type Richter transformation arises from convergent Hodgkin-like immune evasion on a genetically primed CLL/SLL background. Through integrated genomic and tumor microenvironment analyses in a large multicenter cohort, we establish this as a biologically coherent entity and provide rationale for PD-L1/PD-L2-directed therapeutic strategies.