Medicine & Health

This review surveys the design principles and functional architectures of stimuli-responsive nanoprobes, noting that they can achieve context-responsive activation and signal modulation in response to endogenous tumor cues (acidic pH around 6.5-6.8, glutathione at 2-10 mM, enzymatic overexpression, hypoxia below 2% O2, and redox gradients) and exogenous triggers (near-infrared light at 700-1000 nm, magnetic fields, and ultrasound), yielding 5-20-fold signal amplification, and that they can be integrated with multimodal imaging and artificial intelligence for real-time data interpretation and adaptive diagnostics.

In a prospective single-center cohort of 143 patients with clinically localized prostate cancer (cT1-2N0M0) undergoing laparoscopic radical prostatectomy, the study measured preoperative serum bone metabolism indicators such as osteocalcin N-terminal mid-fragment and alkaline phosphatase, extracted biparametric MRI radiomics variables including apparent diffusion coefficient mean and K trans mean, and integrated serum and imaging biomarkers with a multivariate logistic regression model; the integrated model reached an area under the ROC curve of 0.984, significantly outperforming individual biomarkers and imaging features (all p < 0.001), showed clinical net benefit with strong calibration (Hosmer-Lemeshow test p = 0.

Using a design-based research framework, this study implemented an AI-driven immediate feedback system with 89 undergraduate judo therapy students during a four-day observation-based clinical placement, where students submitted daily reflective notes and received rubric-based AI scores and feedback within one minute; all 356 system requests were processed successfully, daily note submission rates exceeded 98% and self-assessment completion rates exceeded 95%, and student questionnaires indicated favorable perceptions; as an exploratory external check, three blinded clinical educators independently rated 120 notes from 30 randomly selected students, showing a moderate rank association (Spearman's rho = .495) but limited absolute agreement (ICC = .

This thesis explores how sensor signals combined with AI models can quantify equine locomotion and respiration, finding that a single limb sensor can accurately classify terrain type even at low sampling rates, that a combination of head, withers, and pelvis sensors can discriminate between sound and lame strides with performance varying by front or hind limb lameness, that vertical ground forces are better estimated from upper-body sensors than limb sensors, that dynamic respiratory rate can be computed from downsampled audio signals, and that explainable AI confirms models rely on kinematic landmarks similar to those used by veterinarians, while also presenting a data collection platform for harness racing horses and publishing an audio dataset.

This work introduces the Virtual Biotech, an organization of AI agents modeled on a drug-development company with agentic divisions spanning target discovery, safety assessment, modality selection, and clinical development, and demonstrates its utility at three drug-development decision points: over 37,000 agents annotated outcomes from 55,984 trials and found that drugs targeting cell-type-specific genes were 48% more likely to reach market with 32% fewer adverse events; it integrated multimodal evidence to propose a therapeutic strategy in lung cancer; and it analyzed a terminated ulcerative colitis trial and inferred potential mechanisms of failure.

Using an on-premise fine-tuned Llama-3.1-70b medication information extraction pipeline, the study automatically extracted medication information from 538 unannotated routine 2012 doctors' letters and compared them with 500 CARDIO:DE letters from 2020/21 (using gold-standard annotations), finding that the DOAC proportion rose from 16.9% to 59.9% while the VKA proportion fell from 37.7% to 9.9%, that the dominant active ingredient within DOACs shifted from rivaroxaban to apixaban, and that manual review showed remaining errors were mainly linked to generic medication mentions and missing medication-reason relations rather than incorrect extraction.

In a 401-patient multicentre European prospective pulmonary arterial hypertension (PAH) cohort, adding right ventricular echocardiographic parameters—especially right ventricular-pulmonary arterial (RV-PA) coupling indices—to the ESC/ERS and REVEAL 2.0 risk scores improved the c-index for morbi-mortality prediction from 0.73 to 0.78 and from 0.74 to 0.79, respectively (P<0.01), with machine learning identifying RV-PA coupling as the most influential dimension.