Publications by authors named "P L Lam"

Background: Paediatric sarcomas, including rhabdomyosarcoma, Ewing sarcoma and osteosarcoma, represent a group of malignancies that significantly contribute to cancer-related morbidity and mortality in children and young adults. These cancers share common challenges, including high rates of metastasis, recurrence or treatment resistance, leading to a 5-year survival rate of approximately 20% for patients with advanced disease stages. Despite the critical need, therapeutic advancements have been limited over the past three decades.

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Observations of dissolved cadmium (dCd) and phosphate (PO) suggest an unexplained loss of dCd to the particulate phase in tropical oxyclines. Here, we compile existing observations of particulate Cd and phosphorus (P), and present new data from the US GEOTRACES GP15 Pacific Meridional Transect to examine this phenomenon from a particulate Cd perspective. We use a simple algorithm to reproduce station depth profiles of particulate Cd and P via regeneration and possible subsurface accumulation.

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Eutectogels have emerged as a promising material for wearable devices due to its superior ionic conductivity, non-volatility, and low cost. Despite numerous efforts, only a limited number of polymers and gelling mechanisms have been successfully employed in the fabrication of eutectogels. In this study, an effective three-dimensional network is developed based on the entanglements of polymer chains, facilitating the formation of an entangled eutectogel.

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Giant cell myocarditis (GCM) and cardiac sarcoidosis share clinical and histologic features, but whether they represent separate processes or lie on an inflammatory cardiomyopathy spectrum is unclear. We present a case of cardiogenic shock thought to be secondary to biopsy-proven GCM with a subsequent post-transplant diagnosis of sarcoidosis through 18-fluorodeoxyglucose positron emission tomography and biopsy.

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Article Synopsis
  • Hepatocellular carcinoma (HCC) has a high mortality rate, and current diagnostic methods like LI-RADS often lead to indeterminate results, complicating accurate diagnosis.
  • Researchers developed four deep learning models using CT scans, finding that the Spatio-Temporal 3D Convolution Network (ST3DCN) performed best, significantly outperforming standard radiological interpretation in identifying HCC.
  • The ST3DCN model demonstrated strong diagnostic accuracy in both internal validation (AUCs up to 0.919) and external testing (AUC of 0.901), indicating its potential as an effective tool for HCC diagnosis.
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