Time and Space Dual-Blockade Strategy for Highly Invasive Nature of Triple-Negative Breast Cancer in Enhanced Sonodynamic Therapy Based on Fe-MOF Nanoplatforms

Triple-negative breast cancer (TNBC), due to its high malignant degree and strong invasion ability, leads to poor prognosis and easy recurrence, so effectively curbing the invasion of TNBC is the key to obtaining the ideal therapeutic effect. Herein, a therapeutic strategy is developed that curbs high invasions of TNBC by inhibiting cell physiological activity and disrupting tumor cell structural function to achieve the time and space dual-blockade. The time blockade is caused by the breakthrough of the tumor-reducing blockade based on the ferroptosis process and the oxidation-toxic free radicals generated by enhanced sonodynamic therapy (SDT). Meanwhile, alkyl radicals from 2,2'-azobis[2-(2-imidazolin-2-yl)propane] dihydrochloride (AIPH) and 1 O2 attacked the organelles of tumor cells under ultrasound (US), reducing the physiological activity of the cells. The attack of free radicals on the cytoskeleton, especially on the proteins of F-actin and its assembly pathway, achieves precise space blockade of TNBC. The damage to the cytoskeleton and the suppression of the repair process leads to a significant decline in the ability of tumor cells to metastasize and invade other organs. In summary, the FTM@AM nanoplatforms have a highly effective killing and invasion inhibition effect on invasive TNBC mediated by ultrasound, showcasing promising clinical transformation potential.

Artificial Intelligence for Surface-Enhanced Raman Spectroscopy

Surface-enhanced Raman spectroscopy (SERS), well acknowledged as a fingerprinting and sensitive analytical technique, has exerted high applicational value in a broad range of fields including biomedicine, environmental protection, food safety among the others. In the endless pursuit of ever-sensitive, robust, and comprehensive sensing and imaging, advancements keep emerging in the whole pipeline of SERS, from the design of SERS substrates and reporter molecules, synthetic route planning, instrument refinement, to data preprocessing and analysis methods. Artificial intelligence (AI), which is created to imitate and eventually exceed human behaviors, has exhibited its power in learning high-level representations and recognizing complicated patterns with exceptional automaticity. Therefore, facing up with the intertwining influential factors and explosive data size, AI has been increasingly leveraged in all the above-mentioned aspects in SERS, presenting elite efficiency in accelerating systematic optimization and deepening understanding about the fundamental physics and spectral data, which far transcends human labors and conventional computations. In this review, the recent progresses in SERS are summarized through the integration of AI, and new insights of the challenges and perspectives are provided in aim to better gear SERS toward the fast track.

Pure laparoscopic versus open left lateral hepatectomy in pediatric living donor liver transplantation: a review and meta-analysis

PURPOSE

The meta-analysis was conducted to evaluate the safety and feasibility of pure laparoscopic left lateral hepatectomy in comparison with open approach for pediatric living donor liver transplantation (LDLT).

METHODS

A systemic literature survey was performed by searching the PubMed, EMBASE and Cochrane Library databases for articles that compared pure laparoscopic left lateral living donor hepatectomy (LLDH) and open left lateral living donor hepatectomy (OLDH) by November 2021. Meta-analysis was performed to assess donors' and recipients' perioperative outcomes using RevMan 5.3 software.

RESULTS

A total of five studies involving 432 patients were included in the analysis. The results demonstrated that LLDH group had significantly less blood loss (WMD = -99.28 ml, 95%CI -152.68 to -45.88, p = 0.0003) and shorter length of hospital stay (WMD = -2.71d, 95%CI -3.78 to -1.64, p < 0.00001) compared with OLDH group. A reduced donor overall postoperative complication rate was observed in the LLDH group (OR = 0.29, 95%CI 0.13-0.64, p = 0.002). In the subgroup analysis, donor bile leakage, wound infection and pulmonary complications were similar between two groups (bile leakage: OR = 1.31, 95%CI 0.43-4.02, p = 0.63; wound infection: OR = 0.38, 95%CI 0.10-1.41, p = 0.15; pulmonary complications: OR = 0.24, 95%CI 0.04-1.41, p = 0.11). For recipients, there were no significant difference in perioperative outcomes between the LLDH and OLDH group, including mortality, overall complications, hepatic artery thrombosis, portal vein and biliary complications.

CONCLUSION

LLDH is a safe and effective alternative to OLDH for pediatric LDLT, reducing invasiveness and benefiting postoperative recovery. Future large-scale multi-center studies are expected to confirm the advantages of LLDH in pediatric LDLT.

Accurate Tumor Subtype Detection with Raman Spectroscopy via Variational Autoencoder and Machine Learning

Accurate diagnosis of cancer subtypes is a great guide for the development of surgical plans and prognosis in the clinic. Raman spectroscopy, combined with the machine learning algorithm, has been demonstrated to be a powerful tool for tumor identification. However, the analysis and classification of Raman spectra for biological samples with complex compositions are still challenges. In addition, the signal-to-noise ratio of the spectra also influences the accuracy of the classification. Herein, we applied the variational autoencoder (VAE) to Raman spectra for downscaling and noise reduction simultaneously. We validated the performance of the VAE algorithm at the cellular and tissue levels. VAE successfully downscaled high-dimensional Raman spectral data to two-dimensional (2D) data for three subtypes of non-small cell lung cancer cells and two subtypes of kidney cancer tissues. Gaussian naïve bayes was applied to subtype discrimination with the 2D data after VAE encoding at both the cellular and tissue levels, significantly outperforming the discrimination results using original spectra. Therefore, the analysis of Raman spectroscopy based on VAE and machine learning has great potential for rapid diagnosis of tumor subtypes.

Raman Nanotags-Guided Intraoperative Sentinel Lymph Nodes Precise Location with Minimal Invasion

The accurate positioning of sentinel lymph node (SLN) by tracers during surgery is an important prerequisite for SLN biopsy. A major problem of traditional tracers in SLN biopsy is the short surgery window due to the fast diffusion of tracers through the lymphatics, resulting in a misjudgment between SLN and second echelon lymph node (2nd LN). Here, a nontoxic Raman nanoparticle tracer, termed gap-enhanced Raman tags (GERTs), for the accurate intraoperative positioning of SLNs with a sufficient surgical time window is designed. In white New Zealand rabbit models, GERTs enable precise identification of SLNs within 10 min, as well as provide the surgeon with a more than 4 h time window to differentiate SLN and 2nd LN. In addition, the ultrahigh sensitivity of GERTs (detection limit is 0.5 × 10-12 m) allows detection of labeled SLNs before surgery, thereby providing preoperative positioning information for minimally invasive surgery. Comprehensive biosafety evaluations carried out in the context of the Food and Drug Administration and International Standard Organization demonstrate no significant toxicity of GERTs, which supports a promising clinical translation opportunity of GERTs for precise SLN identification in breast cancer.