Citations

Abstract Provided herein are novel tetrahydropyridinyl-6,6-bicyclic compounds as 5-HT2A receptor agonists, pharmaceutical compositions, use of such compounds in treating depression, anxiety, substance abuse and headaches, and processes for preparing such compounds.

Abstract INTRODUCTION Centiloid (CL) scaling standardizes amyloid positron emission tomography (PET) quantification across tracers and platforms; however, variability across software implementations may affect diagnostic classification. This study evaluated inter‐software variability and diagnostic performance across five platforms using identical 1 8 F‐florbetapir datasets. METHODS Retrospectively, 192 patients undergoing 1 8 F‐florbetapir PET/computed tomography (CT) and magnetic resonance imaging (MRI) were analyzed. CL values were generated using four US Food and Drug Administration (FDA) ‐cleared platforms and an in‐house Centiloid standard pipeline. Agreement was assessed using intraclass correlation coefficient, with bias and limits of agreement evaluated by linear modeling and Bland–Altman analysis. Diagnostic performance was assessed using receiver operating characteristic (ROC) analysis and classification against visual interpretation. RESULTS Agreement was excellent (intraclass correlation coefficient [ICC] 0.969; 95%CI 0.961–0.975). Some platforms produced systematically higher CL values versus others slightly lower. LoA reached ± 30 CL. Diagnostic accuracy was high (area under the curve [AUC]: 0.949–0.975), with sensitivity 0.923–0.968 and specificity 0.667–0.806. DISCUSSION Despite excellent agreement, systematic differences persist and may affect classification near thresholds, supporting consistent use of a single processing pipeline.

Abstract Large language models (LLMs) and vision‐language models represent a fundamentally different category of artificial intelligence (AI) compared to prior image analysis approaches in digital pathology, which have largely been based on convolutional neural network architectures. This review from the American Society of Cytopathology Clinical Practice Committee examines the current evidence for LLM and vision‐language model applications in cytopathology, including structured reporting, diagnostic assistance, quality control, education, and workflow integration. The distinction between applications with preliminary evidence and those that remain hypothetical is described. A detailed assessment of the challenges that must be addressed before clinical deployment, including hallucination risk, limited explainability, bias, data privacy, validation gaps, and infrastructure barriers is discussed. A review of the regulatory landscape in the United States and European Union as it applies to AI‐enabled software as a medical device is provided. Recommendations addressing cytopathology‐specific benchmarks, multi‐institutional validation, transparent governance, and incremental deployment beginning with low‐risk applications are suggested. In the current environment, LLMs have the potential to augment cytopathology practice, but responsible adoption requires rigorous validation and sustained collaboration among cytopathologists, AI researchers, and regulatory bodies.

ABSTRACT Many real‐world patients with advanced biliary tract cancer (BTC) are excluded by TOPAZ‐1 criteria; this study assessed cisplatin and gemcitabine plus durvalumab (CGD) efficacy and safety in these patients. Data from 1358 patients with advanced BTC treated with first‐line CGD across 55 international centers were retrospectively analyzed. Patients were classified as TOPAZ‐1‐in (meeting all inclusion criteria) or TOPAZ‐1‐out (meeting ≥ 1 exclusion criterion). Primary endpoints were overall survival (OS) and progression‐free survival (PFS); secondary endpoints included objective response rate (ORR), disease control rate (DCR), and safety. Furthermore, OS and PFS of the TOPAZ‐1‐out cohort were compared with reconstructed survival data from the phase III trial. 912 (67.1%) patients were classified as TOPAZ‐1‐in , and 446 (32.9%) as TOPAZ‐1‐out . After a median follow‐up of 14.5 months (95% CI: 13.7–36.5), OS was 16.1 months in TOPAZ‐1‐in and 12.5 months in TOPAZ‐1‐out (HR 0.69, 95% CI: 14.6–16.5, p = 0.0001); PFS was 8.2 versus 6.5 months (HR 0.73, 95% CI: 7.1–8.1, p < 0.0001). Median OS in the TOPAZ‐1‐out cohort (12.5 months) was nearly identical to the phase III trial experimental arm (12.9 months; HR 1.15, p = 0.13); for PFS the HR was 1.12 (95% CI 0.93–1.42; p = 0.09). Among TOPAZ‐1‐out patients, active infection, elevated bilirubin, and ECOG PS > 1 were linked to poorer OS, while no detrimental impact emerged for ALT/AST abnormalities, corticosteroid use, renal or hematologic parameters, or prior surgery within 6 months, suggesting treatment effectiveness was broadly maintained across clinical subgroups. Furthermore, TOPAZ‐1‐out patients experienced no significant increase in adverse events compared with the TOPAZ‐1‐in group. Real‐world data suggest CGD may be effective in TOPAZ‐1‐out patients, with no evident increase in toxicity, supporting the potential expanded use of the regimen in clinical practice.

Point-of-care ultrasound (POCUS) offers unique, real-time advantages for assessing liver disease, benefiting both health care providers and patients. It has been shown to enhance patient care and satisfaction across a variety of medical specialties. Although liver POCUS is practiced widely in many parts of the world, its use in the United States remains limited. Liver POCUS enables rapid assessment of specific hepatic conditions, including both vascular and parenchymal abnormalities, and can play a key role in timely management decision-making—such as guiding the use of diuretics or volume expanders. Once liver POCUS is established, ultrasonography-enabled procedures, such as procedure guidance and shear wave elastography, may be adopted and enrich the practice of hepatology in the future. This paper serves as a summary of the American Association for the Study of Liver Diseases' (AASLD) current position on liver POCUS, offering proposed imaging protocols, recommendations for training and credentialing, and practical guidelines for implementation in clinical settings.

Abstract Objectives To systematically evaluate the certainty of evidence for treatment strategies across age groups and mood phases in bipolar disorder, and develop an open access web platform to facilitate shared decision making. Design Living umbrella review, evaluation, analysis, and communication hub (U-REACH) project. Data sources PubMed, PsycInfo, and Cochrane library databases, from inception to 19 November 2024. Eligibility criteria for selecting studies Systematic reviews with network or pairwise meta-analyses of randomised controlled trials of pharmacological, nutraceutical, psychosocial, brain stimulation, or circadian rhythm based treatments, administered as monotherapy (without concurrent interventions), augmentation treatment (interventions added to an ongoing treatment regimen), or combination treatment (simultaneous initiation of two different interventions), examining any age group, bipolar disorder phase (ie, acute bipolar depression, mania or mixed episodes, or maintenance), treatment, control, or outcome. Results 77 studies met the inclusion criteria (21 network meta-analyses and 56 pairwise meta-analyses), including 116 unique pharmacological (n=74), brain stimulation (n=18), nutraceutical (n=13), psychosocial (n=8), and circadian rhythm based (n=3) treatments as monotherapy, augmentation, or combination therapy, along with five control interventions. These studies covered 133 unique outcomes (45 efficacy outcomes and 88 safety outcomes) resulting in 2510 meta-analyses with Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) ratings of the certainty of the evidence as high (n=236), moderate (n=827), low (n=986), and very low (n=461). A communication hub, the Evidence Based Interventions for Bipolar Disorder (EBI-BD) platform, was developed and the full results are freely available ( https://ebibd-database.org ), including the preference based tool (12 interventions and 17 safety outcomes). Interventions effective across outcomes varied by phases. For bipolar depression, effective interventions in adults were cariprazine, divalproex or valproate, fluoxetine, ketamine (augmentation), lamotrigine, lumateperone, lurasidone, olanzapine, olanzapine with fluoxetine, and quetiapine, whereas effective interventions in children and adolescents were lurasidone and olanzapine with fluoxetine. For mania episodes, effective interventions in adults were aripiprazole, asenapine, carbamazepine, cariprazine, divalproex or valproate, haloperidol, lithium, olanzapine, paliperidone, quetiapine (also augmentation), risperidone (also as augmentation), tamoxifen, and ziprasidone, whereas effective interventions in children and adolescents were aripiprazole, asenapine, olanzapine, quetiapine, and risperidone. For maintenance, interventions effective across outcomes in adults were aripiprazole (also the long acting injectable formulation), asenapine, divalproex or valproate, lithium, olanzapine, group psychoeducation (augmentation), quetiapine, and risperidone long acting injectable formulation. Interventions effective across phases were aripiprazole (also as augmentation and as a long acting injectable formulation), asenapine, cariprazine, cognitive behavioural therapy (augmentation), divalproex or valproate, lamotrigine, lithium, olanzapine (also as augmentation), paliperidone, quetiapine (also as augmentation), and risperidone (also as augmentation and the long acting injectable formulation) (adults). Treatment effects by neuroscience based nomenclature classes are also reported. Conclusions The EBI-BD tool can help clinicians make evidence based, personalised treatment decisions for bipolar disorder. This resource can inform clinical guidelines and provides a foundation for continuously improving bipolar disorder care as new evidence emerges. Trial registration Open Science Framework https://osf.io/pjmvn/ Readers’ note This is a living systematic review and may be updated in the next two years if additional evidence emerges.

Background Intensive care units are high-acuity environments characterized by time pressure, medical complexity, and organizational demands. These conditions may influence clinicians’ ability to provide patient-centered care and contribute to forms of care perceived as fragmented or dehumanizing. Objective To examine how ICU clinicians describe and make sense of the pressures shaping everyday critical care practice, and how these pressures give rise to interacting patterns of care, including those described as hurry, blur, burden, and cruelty. Methods We conducted a qualitative study using focus groups with ICU clinicians from a large academic health system. Discussions were informed by the concept of the pathologies of care as sensitizing concepts. Four focus groups were held with physicians, nurses, advanced practice nurses, and a social worker ( N = 14). Transcripts were analyzed using directed content analysis with both deductive and inductive coding. Results Clinicians described these patterns not as discrete phenomena but as interrelated, context-dependent processes shaped by shared operational pressures such as time constraints, workload, and information demands. These dynamics were often viewed as inherent to the structure and culture of ICU care and were described as co-occurring and compounding. Communication emerged as both a contributor to and a potential mechanism for mitigation, alongside system-level strategies such as reducing documentation burden and leveraging artificial intelligence to support care delivery. Conclusions Patterns reflected in hurry, blur, burden, and cruelty are best understood as emergent properties of complex care systems. Addressing these dynamics may require system-level approaches to improve communication, reduce workload pressures, and support more patient-centered, humanizing care.

Abstract Artificial intelligence is spreading through public health agencies and healthcare systems faster than workforces are trained to govern it. We describe RHAMI (Responsible Health AI Readiness and Maturity Index), the first human-centered automated composite index for health organizations to score AI readiness and maturity as one measurable system of science, economics and ethics, and the physician certification-to-Master’s pathway built to teach what it measures—both designed jointly by frontline public health professionals, clinicians, ethicists, engineers, leaders, and policy-makers in low- and middle-income countries and high-income countries.

Home enteral nutrition (HEN) is essential for patients with a functioning gastrointestinal tract who cannot meet nutritional needs orally, yet outpatient management remains complex, resource-intensive, and supported by a limited evidence base. Artificial intelligence (AI) may augment HEN care by extending monitoring, education, risk assessment, documentation support, and operational coordination into the home environment. Consistent with the narrative review format, this article uses a pragmatic, transparent synthesis of influential HEN-specific literature, relevant clinical nutrition evidence, and background knowledge from adjacent fields, including home healthcare, chronic disease management, oncology nutrition, telehealth, and software regulation. Direct evidence in established HEN populations remains scarce; therefore, most AI applications should be considered hypotheses or early implementation opportunities rather than proven standards of care. The strongest near-term opportunities are clinician-supervised patient education, symptom triage, adherence support, remote monitoring, and workflow automation. Predictive analytics, smart pumps, and precision enteral prescription tools are promising but require prospective HEN-specific validation, interoperability with electronic health records and home-infusion systems, reimbursement pathways, and governance safeguards. Key barriers include dataset bias, limited external validation, alert fatigue, privacy and regulatory concerns, unclear accountability, digital equity, cost uncertainty, and the risk of dehumanizing care. AI should be viewed as a complement to multidisciplinary HEN expertise. Priorities for the near future include HEN registries, standardized outcomes, prospective validation, pragmatic implementation trials, health-economic evaluation, and transparent oversight that preserves clinician accountability and patient-centered care.