Hasil untuk "Medicine"

B. Ritchie, G. Harrison, L. Harrison et al.

S. Fleck

It is my privilege to introduce these two special issues of The Yale Journal of Biology and Medicine containing selected papers presented at the VlIIth International Symposium on Psychotherapy of Schizophrenia: New Approaches to Psychosocial Intervention. The manuscripts published here were selected first by the Editorial and Program Committee, consisting of Drs. Lyman Wynne (Rochester, New York), Daniel P. Schwartz (Stockbridge, Massachusetts), Ira Levine (New Haven, Connecticut), John Gunderson (Boston, Massachusetts), Stephen Fleck (New Haven, Connecticut) and Drs. Yrjo Alanen (Turku, Finland), Jarl Jorstad (Oslo, Norway), Helm Stierlin (Heidelberg, West Germany), Pekka Tienari (Oulu, Finland), Dr. Endre Ugelstad (Oslo), and then by the Editorial Board of the Journal. We are greatly indebted to the Editor of this prestigious journal and to his colleagues for making this publication possible. The Symposium, furthermore, could not have taken place without the additional work of a local arrangements committee: Ms. Mady Chalk, Dr. Charles W. Gardner, Drs. Theodore Lidz, Stan Possick, Robert Rosenheck, and Mr. Lawrence Berger, as well as Dr. Levine and myself. Noteworthy is the fact that this was the first such symposium to be held in the United States at a time, as noted by Dr. Theodore Lidz in his keynote address, when interest in the psychotherapy of schizophrenics is rather low in the United States compared with Europe, a reversal in orientation and research in the two areas over the last quarter-century. Nevertheless, the participation and enthusiasm of the audience of over 300 colleagues, in addition to those involved in the program, conveyed anything but a sense of stagnation or pessimism about the topic. A list of all program contributors is presented at the end of the second special issue. One of the most important features of the conference was five case presentations with a panel of discussants, including one simulated family therapy session. These excellent and well-received presentations are not included in this volume for various reasons, including confidentiality and the severe space limitation imposed by our limited resources. The papers in this volume are arranged in an order that approximates a progression from theoretical and epidemiological topics in the first issue to problems in psychopathological developments and research, to treatment issues and program evaluations, ranging from dyadic psychotherapy to family and group treatments, and finally to a proposal for "global therapy," an action research report from Turku, Finland, representing probably the most comprehensive project with ongoing program evaluation in this field anywhere. It has been my honor to coordinate the work of arranging the symposium program and of this publication. Not only I but the readers are indebted to the committees mentioned above, who deserve our thanks, as do all the participants in the symposium, presenters and audience alike, for this successful event.

L. Brooke

T. Harrison

J. Jacquez

Access to Care, M. Edmunds, M. Coye

A. McColl, Helen Smith, P. White et al.

A. Roses

P. Bonini, M. Plebani, F. Ceriotti et al.

W. Evans, M. Relling

A. Hume

C. May, V. Montori, F. Mair

Dario Sambunjak, S. Straus, A. Marušić

G. Ginsburg, H. Willard

C. Xue, A. Zhang, V. Lin et al.

Michael Cuccarese

Activity cliff prediction - identifying positions where small structural changes cause large potency shifts - has been a persistent challenge in computational medicinal chemistry. This work focuses on a parsimonious definition: which small modifications, at which positions, confer the highest probability of an outcome change. Position-level sensitivity is calculated using 25 million matched molecular pairs from 50 ChEMBL targets across six protein families, revealing that two questions have fundamentally different answers. "Which positions vary most?" is answered by scaffold size alone (NDCG@3 = 0.966), requiring no machine learning. "Which are true activity cliffs?" - where small modifications cause disproportionately large effects, as captured by SALI normalization - requires an 11-feature model with 3D pharmacophore context (NDCG@3 = 0.910 vs. 0.839 random), generalizing across all six protein families, novel scaffolds (0.913), and temporal splits (0.878). The model identifies the cliff-prone position first 53% of the time (vs. 27% random - 2x lift), reducing positions a chemist must explore from 3.1 to 2.1 - a 31% reduction in first-round experiments. Predicting which modification to make is not tractable from structure alone (Spearman 0.268, collapsing to -0.31 on novel scaffolds). The system is released as open-source code and an interactive webapp.

Eva Kozanli, Wanda Han, Tara Smit et al.

Gabrielle Hoyer, Michelle W Tong, Rupsa Bhattacharjee et al.

Precision medicine in musculoskeletal imaging requires scalable measurement infrastructure. We developed a modular system that converts routine MRI into standardized quantitative biomarkers suitable for clinical decision support. Promptable foundation segmenters (SAM, SAM2, MedSAM) were fine-tuned across heterogeneous musculoskeletal datasets and coupled to automated detection for fully automatic prompting. Fine-tuned segmentations yielded clinically reliable measurements with high concordance to expert annotations across cartilage, bone, and soft tissue biomarkers. Using the same measurements, we demonstrate two applications: (i) a three-stage knee triage cascade that reduces verification workload while maintaining sensitivity, and (ii) 48-month landmark models that forecast knee replacement and incident osteoarthritis with favorable calibration and net benefit across clinically relevant thresholds. Our model-agnostic, open-source architecture enables independent validation and development. This work validates a pathway from automated measurement to clinical decision: reliable biomarkers drive both workload optimization today and patient risk stratification tomorrow, and the developed framework shows how foundation models can be operationalized within precision medicine systems.

Haoran Li, Xingye Cheng, Ziyang Huang et al.

Traditional Chinese Medicine diagnosis and treatment principles, established through centuries of trial-and-error clinical practice, directly maps patient-specific symptom patterns to personalised herbal therapies. These empirical holistic mapping principles offer valuable strategies to address remaining challenges of reductionism methodologies in modern biomedicine. However, the lack of a quantitative framework and molecular-level evidence has limited their interpretability and reliability. Here, we present an AI framework trained on ancient and classical TCM formula records to quantify the symptom pattern-herbal therapy mappings. Interestingly, we find that empirical TCM diagnosis and treatment are consistent with the encoding-decoding processes in the AI model. This enables us to construct an interpretable TCM embedding space (TCM-ES) using the model's quantitative representation of TCM principles. Validated through broad and extensive TCM patient data, the TCM-ES offers universal quantification of the TCM practice and therapeutic efficacy. We further map biomedical entities into the TCM-ES through correspondence alignment. We find that the principal directions of the TCM-ES are significantly associated with key biological functions (such as metabolism, immune, and homeostasis), and that the disease and herb embedding proximity aligns with their genetic relationships in the human protein interactome, which demonstrate the biological significance of TCM principles. Moreover, the TCM-ES uncovers latent disease relationships, and provides alternative metric to assess clinical efficacy for modern disease-drug pairs. Finally, we construct a comprehensive and integrative TCM knowledge graph, which predicts potential associations between diseases and targets, drugs, herbal compounds, and herbal therapies, providing TCM-informed opportunities for disease analysis and drug development.

Aliaksandr Marozau, Barbara Karpowicz, Tomasz Kowalewski et al.

Mixed Reality (MR) technologies such as Virtual and Augmented Reality (VR, AR) are well established in medical practice, enhancing diagnostics, treatment, and education. However, there are still some limitations and challenges that may be overcome thanks to the latest generations of equipment, software, and frameworks based on eXtended Reality (XR) by enabling immersive systems that support safer, more controlled environments for training and patient care. Our review highlights recent VR and AR applications in key areas of medicine. In medical education, these technologies provide realistic clinical simulations, improving skills and knowledge retention. In surgery, immersive tools enhance procedural precision with detailed anatomical visualizations. VR-based rehabilitation has shown effectiveness in restoring motor functions and balance, particularly for neurological patients. In mental health, VR has been successful in treating conditions like PTSD and phobias. Although VR and AR solutions are well established, there are still some important limitations, including high costs and limited tactile feedback, which may be overcome with implementing new technologies that may improve the effectiveness of immersive medical applications such as XR, psychophysiological feedback or integration of artificial intelligence (AI) for real-time data analysis and personalized healthcare and training.