Advancements in Intraoperative Near-Infrared Fluorescence Imaging for Accurate Tumor Resection: A Promising Technique for Improved Surgical Outcomes and Patient Survival.

Clear surgical margins for solid tumor resection are essential for preventing cancer recurrence and improving overall patient survival. Complete resection of tumors is often limited by a surgeon's ability to accurately locate malignant tissues and differentiate them from healthy tissue. Therefore, techniques or imaging modalities are required that would ease the identification and resection of tumors by real-time intraoperative visualization of tumors. Although conventional imaging techniques such as positron emission tomography (PET), computed tomography (CT), magnetic resonance imaging (MRI), or radiography play an essential role in preoperative diagnostics, these cannot be utilized in intraoperative tumor detection due to their large size, high cost, long imaging time, and lack of cancer specificity. The inception of several imaging techniques has paved the way to intraoperative tumor margin detection with a high degree of sensitivity and specificity. Particularly, molecular imaging using near-infrared fluorescence (NIRF) based nanoprobes provides superior imaging quality due to high signal-to-noise ratio, deep penetration to tissues, and low autofluorescence, enabling accurate tumor resection and improved survival rates. In this review, we discuss the recent developments in imaging technologies, specifically focusing on NIRF nanoprobes that aid in highly specific intraoperative surgeries with real-time recognition of tumor margins.

Early Predicting Tribocorrosion Rate of Dental Implant Titanium Materials Using Random Forest Machine Learning Models.

Early detection and prediction of bio-tribocorrosion can avert unexpected damage that may lead to secondary revision surgery and associated risks of implantable devices. Therefore, this study sought to develop a state-of-the-art prediction technique leveraging machine learning(ML) models to classify and predict the possibility of mechanical degradation in dental implant materials. Key features considered in the study involving pure titanium and titanium-zirconium (zirconium = 5, 10, and 15 in wt%) alloys include corrosion potential, acoustic emission(AE) absolute energy, hardness, and weight-loss estimates. ML prototype models deployed confirms its suitability in tribocorrosion prediction with an accuracy above 90%. Proposed system can evolve as a continuous structural-health monitoring as well as a reliable predictive modeling technique for dental implant monitoring.

Artificial intelligence and machine learning as a viable solution for hip implant failure diagnosis-Review of literature and in vitro case study.

The digital health industry is experiencing fast-paced research which can provide digital care programs and technologies to enhance the competence of healthcare delivery. Orthopedic literature also confirms the applicability of artificial intelligence (AI) and machine learning (ML) models to medical diagnosis and clinical decision-making. However, implant monitoring after primary surgery often happens with a wellness visit or when a patient complains about it. Neglecting implant design and other technical errors in this scenario, unmonitored circumstances, and lack of post-surgery monitoring may ultimately lead to the implant system's failure and leave us with the only option of high-risk revision surgery. Preventive maintenance seems to be a good choice to identify the onset of an irreversible prosthesis failure. Considering all these aspects for hip implant monitoring, this paper explores existing studies linking ML models and intelligent systems for hip implant diagnosis. This paper explores the feasibility of an alternative continuous monitoring technique for post-surgery implant monitoring backed by an in vitro ML case study. Tribocorrosion and acoustic emission (AE) data are considered based on their efficacy in determining irreversible alteration of implant material to prevent total failures. This study also facilitates the relevance of developing an artificially intelligent implant monitoring methodology that can function with daily patient activities and how it can influence the digital orthopedic diagnosis. AI-based non-invasive hip implant monitoring system enabling point-of-care testing.

Activation of tyrosine hydroxylase by intermittent hypoxia: involvement of serine phosphorylation.

Regulation of tyrosine hydroxylase (TH) by intermittent hypoxia (IH) was investigated in rat pheochromocytoma 12 (PC-12) cells by exposing them to alternating cycles of hypoxia (1% O2, 15 s) and normoxia (21% O2, 3 min) for up to 60 cycles; controls were exposed to normoxia for a similar duration. IH exposure increased dopamine content and TH activity by approximately 42 and approximately 56%, respectively. Immunoblot analysis revealed that comparable levels of TH protein were expressed in normoxic and IH cells. Removal of TH-bound catecholamines and in vitro phosphorylation of TH in cell-free extracts by the catalytic subunit of protein kinase A (PKA) increased TH activity in normoxic but not in IH cells, suggesting possible induction of TH phosphorylation and removal of endogenous inhibition of TH by IH. To assess the role of serine phosphorylation in IH-induced TH activation, TH immunoprecipitates and extracts derived from normoxic and IH cells were probed with anti-phosphoserine and anti-phospho-TH (Ser-40) antibody, respectively. Compared with normoxic cells, total serine and Ser-40-specific phosphorylation of TH were increased in IH cells. IH-induced activation of TH and the increase in total serine and Ser-40-specific phosphorylation of TH were inhibited by Ca2+/calmodulin-dependent protein kinase (CaMK) and PKA-specific inhibitors but not by inhibitors of the extracellular signal-regulated protein kinase pathway, suggesting that IH activates TH in PC-12 cells via phosphorylation of serine residues including Ser-40, in part, by CaMK and PKA. Our results also suggest that IH-induced phosphorylation of TH facilitates the removal of endogenous inhibition of TH, leading to increased synthesis of dopamine.