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High resolution physically based modelling reveals malaria incidence reduction by vector control measures
No full textMalaria continues to cause over 600,000 deaths annually in sub-Saharan Africa, disproportionately affecting children under five. Despite sustained control efforts, transmission remains highly sensitive to local environmental and climatic variability, underscoring the need for physically grounded models capable of capturing these dynamics. To address this challenge, we developed a high-resolution hybrid modeling framework linking WRF/WRF-Hydro and VECTRI. The framework integrates atmospheric, hydrological, ecological, and intervention processes at 1 km and 50 m resolutions and includes a new compartment for insecticide-treated net (ITN) coverage. Using data from 2007–2022 in western Kenya, a period of large-scale ITN deployment, the model reproduced observed malaria trends with a mean monthly deviation of ±100–150 cases. Simulations showed that ITN coverage reduced the entomological inoculation rate and malaria incidence by 58% and 41%, respectively, with the highest efficacy under warm ( C) and moderately wet (150–250 mm) conditions. The findings suggest that integrating environmental process modeling with optimized, targeted control strategies provides a cost-effective and operationally relevant framework for sustainable malaria management under changing climatic conditions
Quantitative dual-tracer PET/CT biomarkers correlate concordant lesion uptake with PSMA-RLT outcomes in mCRPC: a dual-center study
No full textProstate-specific membrane antigen radioligand therapy (PSMA-RLT) has emerged as a promising treatment for metastatic castration-resistant prostate cancer (mCRPC). However, current patient selection methods – largely based on qualitative imaging criteria – may impede precision and efficacy of treatment. We aimed to evaluate the predictive value of quantitative imaging biomarkers derived from dual-tracer [68 Ga]Ga-PSMA-11 and [18F]F-FDG PET/CT, with a focus on concordant lesions.
Methods
Thirty-seven mCRPC patients from two institutions underwent [68 Ga]Ga-PSMA-11 and [18F]F-FDG PET/CT prior to receiving at least two cycles of [177Lu]Lu-PSMA therapy. An automated pipeline enabled lesion segmentation, dual-tracer image fusion, and extraction of quantitative features from concordant (PSMA + /FDG +) and non-concordant lesions. A decision tree model was developed on the Vienna cohort (n = 24) and validated on an independent cohort from Augsburg (n = 13). SHAP analysis was used to identify key predictive features.
Results
The decision tree achieved 95.8% accuracy in the training cohort and 84.6% in external validation. SUVmean of concordant lesions was the most predictive features. Patients with SUVmean[PSMA Concordant] ≥ 12.1 g/mL were more likely to respond. Organ-specific analysis further identified high SUVmax in bone metastases as a negative prognostic marker.
Conclusions
Quantitative metrics from dual-tracer PET, particularly those characterizing concordant lesions, show promise for predicting response to PSMA-RLT. These preliminary findings highlight the potential to move beyond binary eligibility criteria toward a more nuanced, biomarker-driven approach to patient selection
"Siehe, der Name YHWHs kommt aus der Ferne" – der assyrische Hintergrund von Jes 30,27–33
No full textArtificial intelligence‐assisted endoscopy and examiner confidence: a study on human–artificial intelligence interaction in Barrett's esophagus (with video)
No full textObjective: Despite high stand-alone performance, studies demonstrate that artificial intelligence (AI)-supported endoscopic diagnostics often fall short in clinical applications due to human-AI interaction factors. This video-based trial on Barrett's esophagus aimed to investigate how examiner behavior, their levels of confidence, and system usability influence the diagnostic outcomes of AI-assisted endoscopy.
Methods: The present analysis employed data from a multicenter randomized controlled tandem video trial involving 22 endoscopists with varying degrees of expertise. Participants were tasked with evaluating a set of 96 endoscopic videos of Barrett's esophagus in two distinct rounds, with and without AI assistance. Diagnostic confidence levels were recorded, and decision changes were categorized according to the AI prediction. Additional surveys assessed user experience and system usability ratings.
Results: AI assistance significantly increased examiner confidence levels (p < 0.001) and accuracy. Withdrawing AI assistance decreased confidence (p < 0.001), but not accuracy. Experts consistently reported higher confidence than non-experts (p < 0.001), regardless of performance. Despite improved confidence, correct AI guidance was disregarded in 16% of all cases, and 9% of initially correct diagnoses were changed to incorrect ones. Overreliance on AI, algorithm aversion, and uncertainty in AI predictions were identified as key factors influencing outcomes. The System Usability Scale questionnaire scores indicated good to excellent usability, with non-experts scoring 73.5 and experts 85.6.
Conclusions: Our findings highlight the pivotal function of examiner behavior in AI-assisted endoscopy. To fully realize the benefits of AI, implementing explainable AI, improving user interfaces, and providing targeted training are essential. Addressing these factors could enhance diagnostic accuracy and confidence in clinical practice
When and why do psychosis patients discontinue antipsychotics? A data-driven approach using artificial intelligence [Abstract]
No full textAntipsychotic medication is the primary treatment for psychotic disorders, yet nearly half of individuals experiencing a first episode of psychosis discontinue treatment within one year against clinical guidance [1]. Discontinuation may result from poor insight, substance use, negative attitudes toward medication, side effects, cognitive difficulties, or the belief that treatment is no longer needed [2, 3]. Understanding which patients are most at risk of stopping medication, and why, is essential for informing clinical decisions and developing targeted interventions. However, most clinical trials rely on "all-cause discontinuation" (ACD) as a primary endpoint, which aggregates diverse reasons for stopping treatment or leaving the trial and lacks explanatory power. To address this gap, we applied an artificial intelligence (AI)-based clustering approach to identify subgroups of patients with shared characteristics in terms of symptom severity, side effect burden, and medication attitudes at the point of discontinuation. We then trained predictive models using baseline clinical and sociodemographic data to explore whether these subgroups could be identified earlier in care.
Data were drawn from 280 individuals with schizophrenia enrolled in the European Long-Acting Antipsychotics in Schizophrenia Trial (EULAST; NCT02146547) trial [4]. For patients labelled as discontinuing their medication or the trial (ACD), data from the nearest visit within two months of discontinuation were used. Measures included the Clinical Global Impressions (CGI) scale, Positive and Negative Syndrome Scale (PANSS), Medication Adherence Report Scale (MARS), Systematic Monitoring of Adverse events Related to Treatment System (SMARTS), Subjective Wellbeing under Neuroleptic Treatment Scale (SWN), and the digit symbol task from the Wechsler Adult Intelligence Scale (WAIS). We used orthogonal non-negative matrix factorization to identify patient clusters and applied multiclass Support Vector Machine models to predict cluster membership compared to no discontinuation based on baseline clinical and sociodemographic data. We also modelled two standard trial outcomes, ACD and symptomatic remission criteria [5], for comparison.
Of the total sample, 44.2% (n=124) discontinued their medication. Clustering revealed two distinct groups: a "Less Impaired" cluster (n=86), defined by more positive views of medication and better functioning, and a "More Impaired" cluster (n=38), characterized by greater illness severity, more side effects, and more negative attitudes toward medication (p < 0.0001). Baseline predictive models distinguished the More Impaired cluster from the Less Impaired (AUC = 0.64) and from the Non-Discontinuation group (AUC = 0.65). The Less Impaired cluster versus Non-Discontinuation comparison was less accurate (AUC = 0.60). All cluster-based predictions outperformed the ACD prediction (AUC = 0.59), but none exceeded the performance of remission prediction (AUC = 0.73).
This study identified two clinically meaningful subgroups of patients who discontinued treatment, with the More Impaired cluster showing worse symptoms and side effects and being more reliably predicted from baseline characteristics. These findings highlight the potential of AI-driven approaches to move beyond traditional trial endpoints and identify individuals at risk for discontinuation, opening the door to proactive, targeted interventions in early psychosis care
Rezension: Erzählungen / Franz Kafka. Hrsg., kommentiert und mit einem Nachwort von Reiner Stach. - Göttingen: Wallstein-Verlag. - 21 cm [#9816] Bd. 1 (2025). - 392 S. - ISBN 978-3-8353-5903-1
No full textRezension von Band 1 dieser ersten kommentierten Ausgabe der Erzählungen Kafkas, hrsg. von dem Kafka-Biografen Reiner Stach
Rezension: Geschichte der Menschenrechte / Bernd Kannowski. - Köln: Böhlau, 2025. - 476 S.: Ill.; 22 cm. - (Wege zur Rechtsgeschichte) (UTB ; 6399 : Rechtswissenschaften). - ISBN 978-3-8252-6399-7
No full textRezension dieser zweiten umfassenden Geschichte der Menschenrechte seit dem Jahr 2000 des Bayreuther Rechtshistorikers Kannowski
