Medicine & Health

This doctoral thesis, conducted within the EU-funded I-CARE4OLD project, first maps the definition and use of non-pharmacological interventions (NPIs)—a systematic review shows highly heterogeneous and predominantly negatively framed definitions, and data from six countries show underuse of interventions with proven benefits (physical therapy, occupational therapy, psychosocial interventions, social participation) alongside relatively frequent use of physical restraints in some countries—and second, using longitudinal real-world data from the Netherlands and Ontario, Canada, with propensity-based methods, generalized estimating equations, and target trial emulation with causal machine learning, finds that moderate and higher levels of physical activity are associated with beneficial effect

This PhD thesis addresses how causal machine learning can be translated into real-world clinical practice along three lines: for binary treatment effect estimation in clinical contexts it identifies eight key decision points that directly influence treatment effect estimates and proposes a structured framework for designing causal models in clinical settings; for the difficulty of verifying counterfactual predictions it proposes a novel method that grounds average model predictions in a group-level quantity that can be empirically verified using randomized or real-world clinical trial data, and shows that a model with a lower counterfactual error bound can be constructed and that information bottleneck regularization improves treatment effect estimation; for continuous causal machine learn

OncoVision is a privileged-information training framework that uses mammography images and clinical features during training while performing inference from mammographic images alone; built on an attention-based encoder-decoder backbone, it jointly segments four regions of interest (masses, calcifications, axillary findings, and breast tissue) with accuracy exceeding the nnU-Net baseline and predicts ten structured clinical features including BI-RADS category; the authors developed two late-fusion strategies, Independent and Dependent, that integrate imaging, radiomic, and clinical information during training, with radiomic features extracted from predicted masks providing shape, intensity, and texture descriptors that complement the learned CNN representations; in a retrospective multi-re

The paper argues that patient-reported pain location is sometimes diagnostically decisive and sometimes nearly uninformative because it contains three epistemically distinct failures—anatomical multiplexing as a non-identifiable inverse problem, delocalized amplification (clinically central sensitization or nociplastic pain) as a change of generative model, and referred and atypical displacement hypothesized as a systematic, person-dependent shift—which are one Bayesian inference problem failing at the likelihood, the model class, and the group-conditional prior, with a fourth node at the report itself; on this basis the author finds that the published "high-utility" accuracy band leans on overstated specificity, so the gradient is real but flatter than drawn, and attributes the finding th

The work proposes M²PFN, an end-to-end framework that back-propagates task gradients into 3D-MRI and tabular encoders through differentiable inference, aligns the two modalities into a shared subspace matched to the in-context learning prior via disentanglement and a contrastive objective, and folds in a frozen tabular-only prediction through a learnable gated shortcut; on ADNI (n=2240, three-class CN/MCI/AD) it attains 65.55% macro-F1 and 82.21% macro-AUC, surpassing the compared unimodal and multimodal baselines, regresses baseline MMSE by swapping only the head (1250-subject sub-cohort, test MAE 1.743), and achieves the best AUC and lowest MMSE MAE across all baselines on two external cohorts (OASIS-3 and SCAN) with no retraining, transferring even when the cognitive instrument changes.

This work benchmarks three fine-tuned transformer encoders (BanglaBERT, mBERT, XLM-RoBERTa) against GPT-4o mini under zero-shot and few-shot prompting for Bangla medical named entity recognition across the full test set of 3,179 samples, where fine-tuned XLM-RoBERTa reaches an F1 of 0.5959, surpassing the previously reported best of 0.5848, while the language-specific BanglaBERT reaches only 0.4937, and fine-tuned models outperform the optimal prompting configuration by a factor of 3.76.

For cross-modal medical image segmentation with MRI-CT transfer, this work proposes LowBridge under a source-only domain generalization setting where training sees only source-modality samples and testing uses unlabeled target-modality images: a generative model is first trained to recover source images from low-level features such as edges, a segmentation model is then trained separately on the generated source images, and at test time edge features from target images are fed to the pretrained generative model to produce source-style target-domain images for segmentation, achieving performance better than ten existing approaches on multiple public datasets, with ablations indicating compatibility with different generative and segmentation models.

The study proposes a software-only method that reconstructs a 3D oral model from only ten 2D intraoral images captured from different angles, requiring no dedicated hardware; the model is trained on the public Teeth3DS dataset of 950 upper jaw samples and uses MobileNetV2 as the image encoder with Multi-head Attention for multi-view feature fusion, achieving 77.49% accuracy under nearest-neighbor matching with a distance threshold of 0.035, while predicted vertices tend to concentrate in high-density regions of the ground truth, producing uneven point distribution in the reconstructed model.

This review systematically compiles progress in the design, synthesis, and biological evaluation of indole-based small molecules as antitubercular candidates targeting the mycolic acid biosynthesis pathway (MmpL3, InhA, KasA/KasB), listing per-series optimized-compound MIC values (e.g., compounds 20-22 at 0.0195 µg/mL, compound 36a at 0.024 µM, and compound 82 with MIC50 0.015 µM in the MmpL3 direction; compounds 122a at 0.39 µM and 143f at 3.99 µM in the InhA direction) alongside molecular docking results, and noting that many indole series still lack direct biochemical inhibition data such as purified-enzyme IC50/Ki and genetic validation including resistance mutations or target overexpression.