Exploring near-infrared autofluorescence properties in parathyroid tissue: an analysis of fresh and paraffin-embedded thyroidectomy specimens.

Significance: Near-infrared autofluorescence (NIRAF) utilizes the natural autofluorescence of parathyroid glands (PGs) to improve their identification during thyroid surgeries, reducing the risk of inadvertent removal and subsequent complications such as hypoparathyroidism. This study evaluates NIRAF's effectiveness in real-world surgical settings, highlighting its potential to enhance surgical outcomes and patient safety. Aim: We evaluate the effectiveness of NIRAF in detecting PGs during thyroidectomy and central neck dissection and investigate autofluorescence characteristics in both fresh and paraffin-embedded tissues. Approach: We included 101 patients diagnosed with papillary thyroid cancer who underwent surgeries in 2022 and 2023. We assessed NIRAF's ability to locate PGs, confirmed via parathyroid hormone assays, and involved both junior and senior surgeons. We measured the accuracy, speed, and agreement levels of each method and analyzed autofluorescence persistence and variation over 10 years, alongside the expression of calcium-sensing receptor (CaSR) and vitamin D. Results: NIRAF demonstrated a sensitivity of 89.5% and a negative predictive value of 89.1%. However, its specificity and positive predictive value (PPV) were 61.2% and 62.3%, respectively, which are considered lower. The kappa statistic indicated moderate to substantial agreement (kappa = 0.478; P < 0.001 ). Senior surgeons achieved high specificity (86.2%) and PPV (85.3%), with substantial agreement (kappa = 0.847; P < 0.001 ). In contrast, junior surgeons displayed the lowest kappa statistic among the groups, indicating minimal agreement (kappa = 0.381; P < 0.001 ). Common errors in NIRAF included interference from brown fat and eschar. In addition, paraffin-embedded samples retained stable autofluorescence over 10 years, showing no significant correlation with CaSR and vitamin D levels. Conclusions: NIRAF is useful for PG identification in thyroid and neck surgeries, enhancing efficiency and reducing inadvertent PG removals. The stability of autofluorescence in paraffin samples suggests its long-term viability, with false positives providing insights for further improvements in NIRAF technology.

Intraoperative AI-assisted early prediction of parathyroid and ischemia alert in endoscopic thyroid surgery.

BACKGROUND: The preservation of parathyroid glands is crucial in endoscopic thyroid surgery to prevent hypocalcemia and related complications. However, current methods for identifying and protecting these glands have limitations. We propose a novel technique that has the potential to improve the safety and efficacy of endoscopic thyroid surgery. PURPOSE: Our study aims to develop a deep learning model called PTAIR 2.0 (Parathyroid gland Artificial Intelligence Recognition) to enhance parathyroid gland recognition during endoscopic thyroidectomy. We compare its performance against traditional surgeon-based identification methods. MATERIALS AND METHODS: Parathyroid tissues were annotated in 32 428 images extracted from 838 endoscopic thyroidectomy videos, forming the internal training cohort. An external validation cohort comprised 54 full-length videos. Six candidate algorithms were evaluated to select the optimal one. We assessed the model's performance in terms of initial recognition time, identification duration, and recognition rate and compared it with the performance of surgeons. RESULTS: Utilizing the YOLOX algorithm, we developed PTAIR 2.0, which demonstrated superior performance with an AP50 score of 92.1%. The YOLOX algorithm achieved a frame rate of 25.14 Hz, meeting real-time requirements. In the internal training cohort, PTAIR 2.0 achieved AP50 values of 94.1%, 98.9%, and 92.1% for parathyroid gland early prediction, identification, and ischemia alert, respectively. Additionally, in the external validation cohort, PTAIR outperformed both junior and senior surgeons in identifying and tracking parathyroid glands (p < 0.001). CONCLUSION: The AI-driven PTAIR 2.0 model significantly outperforms both senior and junior surgeons in parathyroid gland identification and ischemia alert during endoscopic thyroid surgery, offering potential for enhanced surgical precision and patient outcomes.

Optimizing robotic thyroid surgery: lessons learned from an retrospective analysis of 104 cases.

Background: Robotic assistance in thyroidectomy is a developing field that promises enhanced surgical precision and improved patient outcomes. This study investigates the impact of the da Vinci Surgical System on operative efficiency, learning curve, and postoperative outcomes in thyroid surgery. Methods: We conducted a retrospective cohort study of 104 patients who underwent robotic thyroidectomy between March 2018 and January 2022. We evaluated the learning curve using the Cumulative Sum (CUSUM) analysis and analyzed operative times, complication rates, and postoperative recovery metrics. Results: The cohort had a mean age of 36 years, predominantly female (68.3%). The average body mass index (BMI) was within the normal range. A significant reduction in operative times was observed as the series progressed, with no permanent hypoparathyroidism or recurrent laryngeal nerve injuries reported. The learning curve plateaued after the 37th case. Postoperative recovery was consistent, with no significant difference in hospital stay duration. Complications were minimal, with a noted decrease in transient vocal cord palsy as experience with the robotic system increased. Conclusion: Robotic thyroidectomy using the da Vinci system has demonstrated a significant improvement in operative efficiency without compromising safety. The learning curve is steep but manageable, and once overcome, it leads to improved surgical outcomes and high patient satisfaction. Further research with larger datasets and longer follow-up is necessary to establish the long-term benefits of robotic thyroidectomy.

Efficacy of anlotinib combined with radioiodine to treat scalp metastasis of papillary thyroid cancer: A case report and review of literature.

BACKGROUND: Papillary thyroid cancer (PTC) is one of the well-differentiated thyroid tumors. Cutaneous metastasis from differentiated thyroid cancers occurs in < 1% of primary thyroid carcinomas but produces the worst survival prognosis. The multi-targeting tyrosine kinase inhibitor anlotinib has been approved to treat refractory advanced non-small-cell lung cancer as well as advanced soft-tissue and clear cell sarcomas in China. CASE SUMMARY: In a patient with scalp metastasis caused by PTC, thyroid and skull metastasis tumor sizes were significantly reduced after a trial of neoadjuvant anlotinib therapy for 3 cycles. Anlotinib maintenance medication after thyroidectomy further reduced the metastatic skull tumor size thereby preventing the requirement for craniotomy. CONCLUSION: The outcome of the present trial confirmed the potential of anlotinib therapy to treat scalp metastasis induced by PTC and point the way for the treatment of similar diseases in the future.

Development of Artificial Intelligence for Parathyroid Recognition During Endoscopic Thyroid Surgery.

OBJECTIVE: We aimed to establish an artificial intelligence (AI) model to identify parathyroid glands during endoscopic approaches and compare it with senior and junior surgeons' visual estimation. METHODS: A total of 1,700 images of parathyroid glands from 166 endoscopic thyroidectomy videos were labeled. Data from 20 additional full-length videos were used as an independent external cohort. The YOLO V3, Faster R-CNN, and Cascade algorithms were used for deep learning, and the optimal algorithm was selected for independent external cohort analysis. Finally, the identification rate, initial recognition time, and tracking periods of PTAIR (Artificial Intelligence model for Parathyroid gland Recognition), junior surgeons, and senior surgeons were compared. RESULTS: The Faster R-CNN algorithm showed the best balance after optimizing the hyperparameters of each algorithm and was updated as PTAIR. The precision, recall rate, and F1 score of the PTAIR were 88.7%, 92.3%, and 90.5%, respectively. In the independent external cohort, the parathyroid identification rates of PTAIR, senior surgeons, and junior surgeons were 96.9%, 87.5%, and 71.9%, respectively. In addition, PTAIR recognized parathyroid glands 3.83 s ahead of the senior surgeons (p = 0.008), with a tracking period 62.82 s longer than the senior surgeons (p = 0.006). CONCLUSIONS: PTAIR can achieve earlier identification and full-time tracing under a particular training strategy. The identification rate of PTAIR is higher than that of junior surgeons and similar to that of senior surgeons. Such systems may have utility in improving surgical outcomes and also in accelerating the education of junior surgeons. LEVEL OF EVIDENCE: 3 Laryngoscope, 132:2516-2523, 2022.

An improved method of searching inferior parathyroid gland for the patients with papillary thyroid carcinoma based on a retrospective study.

Objective: Many surgeons knew the importance of parathyroid gland (PG) in the thyroid surgery, but it was even more difficult to be protected. This study aimed at evaluating the effectiveness of the improved method of searching inferior parathyroid gland (IPG). Methods: 213 patients were enrolled and divided into test and control groups according to different methods of searching IPG in the surgery. Consequently, we compared the surgical outcome parameters between the two groups, including the operative time, numbers of PG identifying (PG protection in situ, PG auto-transplantation, and PG accidental removal), numbers of the total lymph node (LN) and metastatic LN, parathyroid hormone (PTH), transient hypoparathyroidism, transient recurrent laryngeal nerve palsy, and postoperative bleeding. Results: We identified 194 (194/196, 98.98%) and 215 (215/230, 93.48%) PGs in the test group and control group, respectively, and there was a significant difference (P = 0.005), and this result was due to IPG identification differences (96/98, 97.96% vs. 100/115, 86.96%, P = 0.004). Meanwhile, there was a lower ratio of IPG auto-transplantation in the test group compared with that in the control group (46.94% vs. 64.35%, P = 0.013). Serum PTH one day after the operation was 3.65 ± 1.86 vs. 2.96 ± 1.64 (P = 0.043) but with no difference at 6 months. There were no differences in metastatic LN and recurrent laryngeal nerve palsy between two groups. Conclusion: The improved method of searching IPG was simple, efficient, and safe, which was easy to be implemented for searching IPG and protecting it well.

Elytra-Mimetic Aligned Composites with Air-Water-Responsive Self-Healing and Self-Growing Capability.

Room-temperature self-healing and self-growing of the exoskeleton with aligned structures in insects has few analogs in synthetic materials. Insect cuticle, such as elytra in beetles, with a typical lightweight lamellar structure, has shown this capability, which is attributed to the accumulation of phenol oxidase with polyphenol and amine-rich compounds in the hard cuticle. In this study, laminar-structure-based intelligence is imitated by incorporating adaptable and growable pyrogallol (PG)-borax dynamic-covalent bonds into a poly(acrylamide)-clay network. The events that lead to crack formation and water accumulation quickly trigger the deprotection of PG. Subsequently, atmospheric O2, as a regeneration source, activates PG oxidative self-polymerization. Multiple permanent and dynamic cross-links, with the involvement of the sacrificed borax, and initiation of a series of intelligent responses occur. The fabricated composites with an aligned lamellar structure exhibit outstanding characteristics, such as air/water-triggered superstrong adhesion, self-repairing, self-sealing and resealing, and reprocessing. Moreover, the strategy endows the composites with a self-growing capability, which leads to a 4- to 10-fold increase in its strength in an outdoor climate (up to 51 MPa). This study could lead to advances in the development of air/water-responsive composite materials for applications such as adaptive barriers.

Synergy of Single-ion Conductive and Thermo-responsive Copolymer Hydrogels Achieving Anti-Arrhenius Ionic Conductivity.

Artificial intelligence sensations have aroused scientific interest from electronic conductors to bio-inspired ionic conductors. The conductivity of electrons decreases with increasing temperature, while the ionic conductivity agrees with an Arrhenius equation or a modified Vogel-Tammann-Fulcher (VTF) equation. Herein, thermo-responsive poly(N-isopropyl amide) (PNIPAm) and single-ion-conducting poly(2-acrylamido-2-methyl-1-propanesulfonic lithium salt) (PAMPSLi) were copolymerized via a facile radical polymerization to demonstrate a very intriguing anti-Arrhenius ionic conductivity behaviour during thermally induced volume-phase transition. These smart hydrogels presented very promising scaffolds for architecting flexible, wearable or advanced functional ionic devices.

Do the cations in clay and the polymer matrix affect quantum dot fluorescent properties?

This paper studied the effects of cations and polymer matrix on the fluorescent properties of quantum dots (QDs). The results indicated that temperature has a greater impact on fluorescence intensity than clay cations (mainly K(+) and Na(+) ). Combined fluorescence lifetime and steady-state spectrometer tests showed that QD lifetimes all decreased when the cation concentration was increased, but the quantum yields were steady at various cation concentrations of 0, 0.05, 0.5 and 1 M. Poly(ethylene oxide) (PEO), poly(vinyl alcohol) (PVA) and diepoxy resin were used to study the effects of polymers on QD lifetime and quantum yield. The results showed that the lifetime for QDs 550 nm in PEO and PVA was 17.33 and 17.12 ns, respectively; for the epoxy resin, the lifetime was 0.74 ns, a sharp decrease from 24.47 ns. The quantum yield for QDs 550 nm changed from 34.22% to 7.45% and 7.81% in PEO and PVA, respectively; for the epoxy resin the quantum yield was 2.25%. QDs 580 nm and 620 nm showed the same results as QDs 550 nm. This study provides useful information on the design, synthesis and application of QDs-polymer luminescent materials. Copyright © 2015 John Wiley & Sons, Ltd.

The synthesis of biodegradable graft copolymer cellulose-graft-poly(L-lactide) and the study of its controlled drug release.

According to the concept of green chemistry and sustainable development, a new biodegradable copolymer comprised of hydrophobic poly(l-lactide) (PLLA) segments and hydrophilic cellulose segment (cellulose-g-PLLA) was designed and synthesized. The structure of the copolymer was characterized by (1)H NMR, FT-IR, (13)C NMR, DSC and WAXD. The cytotoxicity study shows that cellulose-g-PLLA exhibits good biocompatibility. The copolymer can self-assemble into micelles in water with the hydrophobic PLLA segments at the cores of micelles and the hydrophilic cellulose segments as the outer shells. Transmission electron microscopy (TEM) shows that the micelles exhibit nanospheric morphology within a size range of 30-80nm. The drug loaded micelles formed by the copolymer in aqueous media show sustained drug release which indicates their potential applicability in drug carrier.