Insights from the 2nd China intelligent sleep staging competition.

STUDY OBJECTIVES: Artificial intelligence (AI) is quickly advancing in the field of sleep medicine, which bodes well for the potential of actual clinical use. In this study, an analysis of the 2nd China Intelligent Sleep Staging Competition was conducted to gain insights into the general level and constraints of AI-assisted sleep staging in China. METHODS: The outcomes of 10 teams from the children's track and 13 teams from the adult track were investigated in this study. The analysis included overall performance, differences between five different sleep stages, variations across subjects, and performance during stage transitions. RESULTS: The adult track's accuracy peaked at 80.46%, while the children's track's accuracy peaked at 88.96%. On average, accuracy rates stood at 71.43% for children and 68.40% for adults. All results were produced within a mere 5-min timeframe. The N1 stage was prone to misclassification as W, N2, and R stages. In the adult track, significant differences were apparent among subjects (p < 0.05), whereas in the children's track, such differences were not observed. Nonetheless, both tracks experienced a performance decline during stage transitions. CONCLUSIONS: The computational speed of AI is remarkably fast, simultaneously holding the potential to surpass the accuracy of physicians. Improving the machine learning model's classification of the N1 stage and transitional periods between stages, along with bolstering its robustness to individual subject variations, is imperative for maximizing its ability in assisting clinical scoring.

A preliminary study of modified inflatable mediastinoscopic and single-incision plus one-port laparoscopic esophagectomy.

Background: Esophageal malignancies have a high morbidity rate worldwide, and minimally invasive surgery has emerged as the primary approach for treating esophageal cancer. In recent years, there has been increasing discussion about the potential of employing inflatable mediastinoscopic and laparoscopic approaches as an option for esophagectomy. Building on the primary modification of the inflatable mediastinoscopic technique, we introduced a secondary modification to further minimize surgical trauma. Methods: We conducted a retrospective analysis of patients who underwent inflatable mediastinoscopy combined with laparoscopic esophagectomy at the Second Affiliated Hospital of Naval Medical University from March 2020 to March 2023. The patients were allocated to the following two groups: the traditional (primary modification) group, and the secondary modification group. Operation times, intraoperative bleeding, and postoperative complications were compared between the groups. Results: The procedure was successfully performed in all patients, and conversion to open surgery was not required in any case. There were no statistically significant differences in the surgical operation time, intraoperative bleeding, number of dissected lymph nodes, and rate of postoperative anastomotic leakage between the two groups. However, a statistically significant difference was observed in the length of the mobilized esophagus between the two groups. The mobilization of esophagus to the level of diaphragmatic hiatus via the cervical incision was successfully achieved in more patients in the secondary modification group than the primary modification group. Conclusions: Inflatable mediastinoscopy combined with single-incision plus one-port laparoscopic esophagectomy is a safe and effective surgical procedure. The use of a 5-mm flexible endoscope, ultra-long five-leaf forceps, and LigaSure Maryland forceps facilitates esophageal mobilization and lymph node dissection through a single cervical incision.

Research on a machine learning-based adaptive and efficient screening model for psychological symptoms of community correctional prisoners.

Community correction institutions in China frequently employ the Symptom Checklist-90 (SCL-90) and the health survey brief (SF-12) as primary tools for psychological assessment of community correctional prisoners. However, in practical application, the SCL-90 Checklist faces issues such as complex item numbers, overall low cultural level of the subjects, and insufficient professional level of the administrators. The SF-12 health survey brief, as a preliminary screening tool, although only has 12 questions, to some extent simplifies the evaluation process and improves work efficiency, it is prone to missed screening. The research team collected 17-dimensional basic characteristic data and corresponding SCL-90 and SF-12 data from 25,480 samples of community correctional prisoners in Zhejiang Province, China. This study explored the application of multi-label multi-classification algorithms and oversampling techniques in building machine learning models to delve into the correlation between the psychological health risks of community correctional prisoners and their characteristic data. Inspired by computerized adaptive testing (CAT), we constructed an adaptive and efficient screening model for community correctional prisoners through experimental comparisons, based on the binary relevance algorithm with sample oversampling. This screening model personalize the assessment process by dynamically matching participants with the most relevant subset (s) of the nine dimensions of the SCL-90 based on their individual characteristics. Thus, adaptive dynamic simplification and personalized recommendation of the SCL-90 scale between question groups were achieved for the specific group of community correctional prisoners. As a screening tool for psychological symptoms of community correctional prisoners, this model significantly simplifies the number of questions compared to SCL-90, with a simplification rate of up to 65%. However, it achieves this simplification while maintaining excellent performance. The accuracy reached 0.66, with a sensitivity of 0.754, and an F1 score of 0.649. This innovation simplified the assessment process, reduced the assessment time, improved work efficiency, and enhanced the ability to judge the specificity of community correctional prisoners population. Compared to the SF-12, although the simplification rate and accuracy of the model are slightly lower than those of the SF-12, the sensitivity increased by 42.26%, and the F1 score improved by 15.28%. This means the model greatly reduces the possibility of missed screening, effectively preventing prisoners with abnormal psychological or mental states from losing control due to missed screening, and even committing suicide, self injury, or injuring others.

ECGAN-Assisted ResT-Net Based on Fuzziness for OSA Detection.

OBJECTIVE: Growing attention has been paid recently to electrocardiogram (ECG) based obstructive sleep apnea (OSA) detection, with some progresses been made on this topic. However, the lack of data, low data quality, and incomplete data labeling hinder the application of deep learning to OSA detection, which in turn affects the overall generalization capacity of the network. METHODS: To address these issues, we propose the ResT-ECGAN framework. It uses a one-dimensional generative adversarial network (ECGAN) for sample generation, and integrates it into ResTNet for OSA detection. ECGAN filters the generated ECG signals by incorporating the concept of fuzziness, effectively increasing the amount of high-quality data. ResT-Net not only alleviates the problems caused by deepening the network but also utilizes multihead attention mechanisms to parallelize sequence processing and extract more valuable OSA detection features by leveraging contextual information. RESULTS: Through extensive experiments, we verify that ECGAN can effectively improve the OSA detection performance of ResT-Net. Using only ResT-Net for detection, the accuracy on the Apnea-ECG and private databases is 0.885 and 0.837, respectively. By adding ECGAN-generated data augmentation, the accuracy is increased to 0.893 and 0.848, respectively. CONCLUSION AND SIGNIFICANCE: Comparing with the state-of-the-art deep learning methods, our method outperforms them in terms of accuracy. This study provides a new approach and solution to improve OSA detection in situations with limited labeled samples.

Characteristics of ADHD Symptoms and EEG Theta/Beta Ratio in Children With Sleep Disordered Breathing.

Objectives. This study aimed to explore parent-reported symptoms of attention deficit-hyperactivity disorder (ADHD) and sleep electroencephalogram (EEG) theta/beta ratio (TBR) characteristics in children with sleep disordered breathing (SDB). Methods. The parents of children (aged 6-11 years) with SDB (n = 103) and healthy controls (n = 28) completed the SNAP-IV questionnaire, and children underwent overnight polysomnography. Children with SDB were grouped according to obstructive apnea/hypopnea index: primary snoring, mild, and moderate-severe obstructive sleep apnea (OSA) groups. The TBR in non-rapid eye movement (NREM) periods in three sleep cycles was analyzed. Results. Children with SDB showed worse ADHD symptoms compared with the healthy control. There was no intergroup difference in TBR. The time-related decline in TBR observed in the control, primary snoring and mild OSA groups, which was not observed in the moderate-severe OSA group. Overnight transcutaneous oxygen saturation was negatively associated with the hyperactivity/impulsivity score of ADHD symptom. The global TBR during the NREM period in the first sleep cycle was positively correlated with inattention score. Conclusion. Children with SDB showed more ADHD inattention symptoms than the healthy control. Although we found no difference in TBR among groups, we found significant main effect for NREM period. There existed a relationship between hypoxia, TBR, and scores of ADHD symptoms. Hence, it was speculated that TBR can reflect the nocturnal electrophysiological manifestations in children with SDB, which may be related to daytime ADHD symptoms.

Pediatric obstructive sleep apnea diagnosis: leveraging machine learning with linear discriminant analysis.

Objective: The objective of this study was to investigate the effectiveness of a machine learning algorithm in diagnosing OSA in children based on clinical features that can be obtained in nonnocturnal and nonmedical environments. Patients and methods: This study was conducted at Beijing Children's Hospital from April 2018 to October 2019. The participants in this study were 2464 children aged 3-18 suspected of having OSA who underwent clinical data collection and polysomnography(PSG). Participants' data were randomly divided into a training set and a testing set at a ratio of 8:2. The elastic net algorithm was used for feature selection to simplify the model. Stratified 10-fold cross-validation was repeated five times to ensure the robustness of the results. Results: Feature selection using Elastic Net resulted in 47 features for AHI ≥5 and 31 features for AHI ≥10 being retained. The machine learning model using these selected features achieved an average AUC of 0.73 for AHI ≥5 and 0.78 for AHI ≥10 when tested externally, outperforming models based on PSG questionnaire features. Linear Discriminant Analysis using the selected features identified OSA with a sensitivity of 44% and specificity of 90%, providing a feasible clinical alternative to PSG for stratifying OSA severity. Conclusions: This study shows that a machine learning model based on children's clinical features effectively identifies OSA in children. Establishing a machine learning screening model based on the clinical features of the target population may be a feasible clinical alternative to nocturnal OSA sleep diagnosis.

Inhibition of PRKAA/AMPK (Ser485/491) phosphorylation by crizotinib induces cardiotoxicity via perturbing autophagosome-lysosome fusion.

Crizotinib, a small-molecule tyrosine kinase inhibitor targeting ALK, MET and ROS1, is the first-line drug for ALK-positive metastatic non-small cell lung cancer and is associated with severe, sometimes fatal, cases of cardiac failure, which increases the risk of mortality. However, the underlying mechanism remains unclear, which causes the lack of therapeutic strategy. We established in vitro and in vivo models for crizotinib-induced cardiotoxicity and found that crizotinib caused left ventricular dysfunction, myocardial injury and pathological remodeling in mice and induced cardiomyocyte apoptosis and mitochondrial injury. In addition, we found that crizotinib prevented the degradation of MET protein by interrupting autophagosome-lysosome fusion and silence of MET or re-activating macroautophagy/autophagy flux rescued the cardiomyocytes death and mitochondrial injury caused by crizotinib, suggesting that impaired autophagy activity is the key reason for crizotinib-induced cardiotoxicity. We further confirmed that recovering the phosphorylation of PRKAA/AMPK (Ser485/491) by metformin re-activated autophagy flux in cardiomyocytes and metformin rescued crizotinib-induced cardiomyocyte injury and cardiac complications. In summary, we revealed a novel mechanism for crizotinib-induced cardiotoxicity, wherein the crizotinib-impaired autophagy process causes cardiomyocyte death and cardiac injury by inhibiting the degradation of MET protein, demonstrated a new function of impeded autophagosome-lysosome fusion in drugs-induced cardiotoxicity, pointed out the essential role of the phosphorylation of PRKAA (Ser485/491) in autophagosome-lysosome fusion and confirmed metformin as a potential therapeutic strategy for crizotinib-induced cardiotoxicity.Abbreviations and Acronyms: AAV: adeno-associated virus; ACAC/ACC: acetyl-Co A carboxylase; AMP: adenosine monophosphate; AMPK: AMP-activated protein kinase; ATG5: autophagy related 5; ATG7: autophagy related 7; CHX: cycloheximide; CKMB: creatine kinase myocardial band; CQ: chloroquine; c-PARP: cleaved poly (ADP-ribose) polymerase; DAPI: 4'6-diamidino-2-phenylindole; EF: ejection fraction; FOXO: forkhead box O; FS: fractional shortening; GSEA: gene set enrichment analysis; H&E: hematoxylin and eosin; HF: heart failure; HW: TL: ratio of heart weight to tibia length; IR: ischemia-reperfusion; KEGG: Kyoto encyclopedia of genes and genomes; LAMP2: lysosomal-associated membrane protein 2; LDH: lactate dehydrogenase; MCMs: mouse cardiomyocytes; MMP: mitochondrial membrane potential; mtDNA: mitochondrial DNA; MYH6: myosin, heavy peptide 6, cardiac muscle, alpha; MYH7: myosin, heavy peptide 7, cardiac muscle, beta; NPPA: natriuretic peptide type A; NPPB: natriuretic peptide type B; PI: propidium iodide; PI3K: phosphoinositide 3-kinase; PRKAA/AMPKα: protein kinase AMP-activated catalytic subunit alpha; qPCR: quantitative real-time PCR; SD: standard deviation; SRB: sulforhodamine B; TKI: tyrosine kinase inhibitor; WGA: wheat germ agglutinin.

Impaired declarative memory consolidation in children with REM sleep-related obstructive sleep apnea.

STUDY OBJECTIVES: We explored whether declarative memory consolidation is impaired in children with rapid eye movement sleep-related obstructive sleep apnea (REM-OSA) and investigated the correlation between memory consolidation and sleep-related respiratory parameters. METHODS: Participants were children with habitual snoring aged 6-14 years and control children. Participants underwent polysomnography and declarative memory testing. Participants with snoring were categorized as primary snoring (PS), non-rapid eye movement sleep-related obstructive sleep apnea (NREM-OSA), stage-independent (SI)-OSA, and REM-OSA according to obstructive apnea-hypopnea index (OAHI), OAHI in REM sleep (OAHIREM), and OAHI in NREM sleep (OAHINREM). Declarative memory consolidation level was assessed by recall and recognition rates. RESULTS: There were 34 controls and 228 children with sleep-disordered breathing: 73 PS, 48 NREM-OSA, 59 SI-OSA, and 48 REM-OSA. Total arousal index was lower in the REM-OSA group than in the NREM-OSA group. In all groups, retest scores were higher than immediate test scores. Recall consolidation in PS, SI-OSA, and REM-OSA groups was lower than for controls and lower in REM-OSA than in NREM-OSA. There were no correlations between recall consolidation or recognition consolidation and OAHI, OAHINREM, oxygen desaturation index in REM sleep, total arousal index, or REM sleep percent. Recognition consolidation was negatively correlated with OAHIREM. CONCLUSIONS: Memory consolidation is impaired in children with REM-OSA compared with NREM-OSA and controls. There was no significant correlation between memory consolidation and OAHI, and recognition consolidation was negatively correlated with OAHIREM. It is important to pay attention to the OSA subtype in children. CITATION: Tang Y, Yang C, Wang C, Wu Y, Xu Z, Ni X. Impaired declarative memory consolidation in children with REM sleep-related obstructive sleep apnea. J Clin Sleep Med. 2024;20(3):417-425.

A Semi-Supervised Multi-Scale Arbitrary Dilated Convolution Neural Network for Pediatric Sleep Staging.

Sleep staging is essential for assessing sleep quality and diagnosing sleep disorders. However, sleep staging is a labor-intensive process, making it arduous to obtain large quantities of high-quality labeled data for automatic sleep staging. Meanwhile, most of the research on automatic sleep staging pays little attention to pediatric sleep staging. To address these challenges, we propose a semi-supervised multi-scale arbitrary dilated convolution neural network (SMADNet) for pediatric sleep staging using the scalogram with a high height-to-width ratio generated by the continuous wavelet transform (CWT) as input. To extract more extended time dimensional feature representations and adapt to scalograms with a high height-to-width ratio in SMADNet, we introduce a multi-scale arbitrary dilation convolution block (MADBlock) based on our proposed arbitrary dilated convolution (ADConv). Finally, we also utilize semi-supervised learning as the training scheme for our network in order to alleviate the reliance on labeled data. Our proposed model has achieved performance comparable to state-of-the-art supervised learning methods with 30% labels. Our model is tested on a private pediatric dataset and achieved 79% accuracy, 72% kappa, and 75% MF1. Therefore, our model demonstrates a powerful feature extraction capability and has achieved performance comparable to state-of-the-art supervised learning methods with a small number of labels.

Identification of novel biomarkers in obstructive sleep apnea via integrated bioinformatics analysis and experimental validation.

Background: Obstructive sleep apnea (OSA) is a complex and multi-gene inherited disease caused by both genetic and environmental factors. However, due to the high cost of diagnosis and complex operation, its clinical application is limited. This study aims to explore potential target genes associated with OSA and establish a corresponding diagnostic model. Methods: This study used microarray datasets from the Gene Expression Omnibus (GEO) database to identify differentially expressed genes (DEGs) related to OSA and perform functional annotation and pathway analysis. The study employed multi-scale embedded gene co-expression network analysis (MEGENA) combined with least absolute shrinkage and selection operator (LASSO) regression analysis to select hub genes and construct a diagnostic model for OSA. In addition, the study conducted correlation analysis between hub genes and OSA-related genes, immunoinfiltration, gene set enrichment analysis (GSEA), miRNA network analysis, and identified potential transcription factors (TFs) and targeted drugs for hub genes. Finally, the study used chronic intermittent hypoxia (CIH) mouse model to simulate OSA hypoxic conditions and verify the expression of hub genes in CIH mice. Results: In this study, a total of 401 upregulated genes and 275 downregulated genes were identified, and enrichment analysis revealed that these differentially expressed genes may be associated with pathways such as vasculature development, cellular response to cytokine stimulus, and negative regulation of cell population proliferation. Through MEGENA combined with LASSO regression, seven OSA hub genes were identified, including C12orf54, FOS, GPR1, OR9A4, MYO5B, RAB39B, and KLHL4. The diagnostic model constructed based on these genes showed strong stability. The expression levels of hub genes were significantly correlated with the expression levels of OSA-related genes and mainly acted on pathways such as the JAK/STAT signaling pathway and the cytosolic DNA-sensing pathway. Drug-target predictions for hub genes were made using the Connectivity Map (CMap) database and the Drug-Gene Interaction database (Dgidb), which identified targeted therapeutic drugs for the hub genes. In vivo experiments showed that the hub genes were all decreasing in the OSA mouse model. Conclusions: This study identified novel biomarkers for OSA and established a reliable diagnostic model. The transcriptional changes identified may help to reveal the pathogenesis, mechanisms, and sequelae of OSA.

Blend U-Net: Redesigning Skip Connections to Obtain Multiscale Features for Lung CT Images Segmentation.

BACKGROUND: Lung cancer is a pervasive and persistent issue worldwide, with the highest morbidity and mortality among all cancers for many years. In the medical field, computer tomography (CT) images of the lungs are currently recognized as the best way to help doctors detect lung nodules and thus diagnose lung cancer. U-Net is a deep learning network with an encoder-decoder structure, which is extensively employed for medical image segmentation and has derived many improved versions. However, these advancements do not utilize various feature information from all scales, and there is still room for future enhancement. METHODS: In this study, we proposed a new model called Blend U-Net, which incorporates nested structures, redesigned long and short skip connections, and deep supervisions. The nested structures and the long and short skip connections combined characteristic information of different levels from feature maps in all scales, while the deep supervision learning hierarchical representations from all-scale concatenated feature maps. Additionally, we employed a mixed loss function to obtain more accurate results. RESULTS: We evaluated the performance of the Blend U-Net against other architectures on the publicly available Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) dataset. Moreover, the accuracy of the segmentation was verified by using the dice coefficient. Blend U-Net with a boost of 0.83 points produced the best outcome in a number of baselines. CONCLUSION: Based on the results, our method achieves superior performance in terms of dice coefficient compared to other methods and demonstrates greater proficiency in segmenting lung nodules of varying sizes.

Characteristics of the Attentional Network in Children with Sleep-Disordered Breathing.

Purpose: To explore the characteristics of the attentional network and related factors in children with sleep-disordered breathing (SDB). Patients and Methods: A total 228 children (200 children aged 6-10 years with snoring or mouth breathing, admitted to our hospital from May 2020 to July 2022, and 28 healthy children recruited from the community as the control group) were enrolled. All participants underwent polysomnography (PSG) and completed the ADHD rating scale and child version of the Attention Network Test. According to their SDB history and obstructive apnea hypopnea index (OAHI), the participants were divided into control (n = 28), primary snoring (PS; n = 67) and obstructive sleep apnea (OSA; n = 133) groups. Results: The OSA and PS groups were younger than controls (P < 0.05). The proportion of boys was higher in the OSA than control group (P < 0.05). Body mass index was higher in the OSA than control and PS groups (P < 0.01). Attention deficit and hyperactive impulsivity scores were independently associated with the OAHI (P < 0.001). The efficiency of the alerting network was higher in the OSA than in controls (P = 0.020), but was not correlated with OAHI after adjusting for age, sex and SDB history duration (P > 0.05). Conclusion: Children with OSA have impaired attention, characterized by excessive alerting network activation. However, alerting network efficiency did not change linearly with disease severity. More research is needed to elucidate the neural mechanisms underlying attention deficits in pediatric OSA.

Development and validation of the narcolepsy severity scale in school aged children.

OBJECTIVE: To develop and psychometrically test the pediatric narcolepsy severity scale (P-NSS) for pediatric with narcolepsy type 1 (NT1). METHODS: Item pool was formed based on literature review, clinical judgement of the expert panel and input of the narcoleptic patients and their parents. Psychometric properties were evaluated after applying the P-NSS in a sample of 200 patients (8-18 years age) with narcolepsy. Analyses included item analysis, validity analysis and reliability analysis. RESULTS: P-NSS consisted four factors with a total of 17 items. Exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) revealed four distinct and theoretically coherent factors, explaining 63.4% of the total variance. The fitting results of the CFA model were χ2/dƒ = 2.235, GFI = 0.876, AGFI = 0.822, RMSEA = 0.079, TLI = 0.908, CFI = 0.927. P-NSS score is correlated with Pediatric Daytime Sleepiness Scale (r = 0.512, P < 0.01), Epworth Sleepiness Scale for Children and Adolescents (r = 0.355, P < 0.01) and Narcolepsy quality-of-life instrument with 21 questions (r = -0.512, P < 0.01). Cronbach's α coefficient for P-NSS and four dimensions were from 0.732 to 0.915. The split-half reliability was 0.882 (P < 0.01). CONCLUSION: P-NSS is proved to be a reliable and valid measure for Chinese children with NT1. It may serve in China as a valuable and easily accessible outcome measure for using in narcolepsy trials, the clinic with improved responsiveness and long term follow-up.

Chloroquine Intervenes Nephrotoxicity of Nilotinib through Deubiquitinase USP13-Mediated Stabilization of Bcl-XL.

Nephrotoxicity has become prominent due to the increase in the clinical use of nilotinib, a second-generation BCR-ABL1 inhibitor in the first-line treatment of Philadelphia chromosome-positive chronic myeloid leukemia. To date, the mechanism of nilotinib nephrotoxicity is still unknown, leading to a lack of clinical intervention strategies. Here, it is found that nilotinib could induce glomerular atrophy, renal tubular degeneration, and kidney fibrosis in an animal model. Mechanistically, nilotinib induces intrinsic apoptosis by specifically reducing the level of BCL2 like 1 (Bcl-XL) in both vascular endothelial cells and renal tubular epithelial cells, as well as in vivo. It is confirmed that chloroquine (CQ) intervenes with nilotinib-induced apoptosis and improves mitochondrial integrity, reactive oxygen species accumulation, and DNA damage by reversing the decreased Bcl-XL. The intervention effect is dependent on the alleviation of the nilotinib-induced reduction in ubiquitin specific peptidase 13 (USP13) and does not rely on autophagy inhibition. Additionally, it is found that USP13 abrogates cell apoptosis by preventing excessive ubiquitin-proteasome degradation of Bcl-XL. In conclusion, the research reveals the molecular mechanism of nilotinib's nephrotoxicity, highlighting USP13 as an important regulator of Bcl-XL stability in determining cell fate, and provides CQ analogs as a clinical intervention strategy for nilotinib's nephrotoxicity.

An update on Alectinib: a first line treatment for ALK-positive advanced lung cancer.

INTRODUCTION: Alectinib is a second-generation, anaplastic lymphoma kinase (ALK) tyrosine kinase inhibitor (TKI) for the treatment of ALK+ non-small cell lung cancer (NSCLC) and is able to induce significant and durable CNS responses. However, long-term use of alectinib has been clinically reported to cause some serious and even life-threatening adverse events. There are currently no effective interventions for its adverse events, and this undoubtedly leads to delays in patient treatment and limits its long-term clinical use. AREAS COVERED: Based on the clinical trials conducted so far, we summarize the efficacy and adverse events that occurred, especially those related to cardiovascular disorders, gastrointestinal disorders, hepatobiliary disorders, musculoskeletal and connective tissue disorders, skin and subcutaneous tissue disorders, and respiratory disorders. The factors that may influence alectinib selection are also described. Findings are based on a PubMed literature search of clinical and basic science research papers spanning 1998-2023. EXPERT OPINION: The significant prolongation of patient survival compared with first-generation ALK inhibitor suggests its potential as a first-line treatment for the NSCLC, but the severe adverse events of alectinib limit its long-term clinical use. Future research should focus on the exact mechanisms of these toxicities, how to alleviate the adverse events caused by alectinib clinically, and the development of next-generation drugs with reduced toxicities.

Crizotinib induces pulmonary toxicity by blocking autophagy flux in alveolar epithelial cells.

Crizotinib is the first-line drug for advanced non-small cell lung cancer with the abnormal expression of anaplastic lymphoma kinase gene. Severe, life-threatening, or fatal interstitial lung disease/pneumonia has been reported in patients treated with crizotinib. The clinical benefit of crizotinib is limited by its pulmonary toxicity, but the underlying mechanisms have not been adequately studied, and protective strategies are relatively scarce. Here, we established an in vivo mouse model in which crizotinib was continuously administered to C57BL/6 at 100 mg/kg/day for 6 weeks and verified that crizotinib induced interstitial lung disease in vivo, which was consistent with the clinical observations. We further treated BEAS-2B and TC-1 cells, the alveolar epithelial cell lines, with crizotinib and found the increased apoptosis rate. We proved that crizotinib-blocked autophagic flux caused apoptosis of the alveolar epithelial cells and then promoted the recruitment of immune cells, suggesting that limited autophagy activity was the key reason for pulmonary injury and inflammation caused by crizotinib. Subsequently, we found that metformin could reduce the macrophage recruitment and pulmonary fibrosis by recovering the autophagy flux, thereby ameliorating impaired lung function caused by crizotinib. In conclusion, our study revealed the mechanism of crizotinib-induced apoptosis of alveolar epithelial cells and activation of inflammation during the onset of pulmonary toxicity and provided a promising therapeutic strategy for the treatment of crizotinib-induced pulmonary toxicity.

Regorafenib inhibits EphA2 phosphorylation and leads to liver damage via the ERK/MDM2/p53 axis.

The hepatotoxicity of regorafenib is one of the most noteworthy concerns for patients, however the mechanism is poorly understood. Hence, there is a lack of effective intervention strategies. Here, by comparing the target with sorafenib, we show that regorafenib-induced liver injury is mainly due to its nontherapeutic target Eph receptor A2 (EphA2). EphA2 deficiency attenuated liver damage and cell apoptosis under regorafenib treatment in male mice. Mechanistically, regorafenib inhibits EphA2 Ser897 phosphorylation and reduces ubiquitination of p53 by altering the intracellular localization of mouse double minute 2 (MDM2) by affecting the extracellular signal-regulated kinase (ERK)/MDM2 axis. Meanwhile, we found that schisandrin C, which can upregulate the phosphorylation of EphA2 at Ser897 also has protective effect against the toxicity in vivo. Collectively, our findings identify the inhibition of EphA2 Ser897 phosphorylation as a key cause of regorafenib-induced hepatotoxicity, and chemical activation of EphA2 Ser897 represents a potential therapeutic strategy to prevent regorafenib-induced hepatotoxicity.

Development and safety of PI3K inhibitors in cancer.

The phosphatidylinositol 3-kinase (PI3K) signalling pathway regulates cell survival, proliferation, migration, metabolism and other vital cellular life processes. In addition, activation of the PI3K signalling pathway is important for cancer development. As a result, a variety of PI3K inhibitors have been clinically developed to treat malignancies. Although several PI3K inhibitors have received approval from the Food and Drug Administration (FDA) for significant antitumour activity, frequent and severe adverse effects have greatly limited their clinical application. These toxicities are mostly on-target and immune-mediated; nevertheless, the underlying mechanisms are still unclear. Current management usually involves intervention through symptomatic treatment, with discontinuation if toxicity persists. Therefore, it is necessary to comprehensively understand these adverse events and ensure the clinical safety application of PI3K inhibitors by establishing the most effective management guidelines, appropriate intermittent dosing regimens and new combination administration. Here, the focus is on the development of PI3K inhibitors in cancer therapy, with particular emphasis on isoform-specific PI3K inhibitors. The most common adverse effects of PI3K inhibitors are also covered, as well as potential mechanisms and management approaches.

A new preoperative localization of pulmonary nodules guided by mixed reality: a pilot study of an animal model.

Background: With the popularity of high-resolution computed tomography (HRCT), more and more pulmonary nodules are being discovered. Video-assisted thoracoscopic surgery (VATS) has become the first choice for surgical treatment of pulmonary nodules. The use of accurate preoperative localization is crucial for successful resection in VATS. At present, there are many kinds of preoperative localization methods, but there are certain disadvantages. This study aimed to evaluate the feasibility and safety of mixed reality (MR)-guided pulmonary nodules localization, which is a new method that can benefit patients to a greater extent. Methods: By constructing an animal model of pulmonary nodules localization, 28 cases of pulmonary nodules were located by MR-guided localization. We recorded the localization accuracy, localization time, insertion attempts, and incidence of complications related to localization under MR-guidance. Results: All 28 nodules were successfully located: the deviation of MR-guided localization was 5.71±2.59 mm, localization time was 8.07±1.44 min, and insertion attempts was 1. A pneumothorax and localizer dislodgement occurred in 1 case, respectively. Conclusions: Since preoperative localization is critical for VATS resection of pulmonary nodules, we investigated a new localization method. As indicated by our study, MR-guided localization of pulmonary nodules is feasible and safe, which is worthy of further research and promotion. We have also registered corresponding clinical trials to further investigate and help to improve our understanding of this technique.

The participation of non-canonical autophagic proteins in the autophagy process and their potential as therapeutic targets.

INTRODUCTION: Autophagy is a conserved catabolic process that helps recycle intracellular components to maintain homeostasis. The completion of autophagy requires the synergistic effect of multiple canonical autophagic proteins. Defects in autophagy machinery have been reported to promote diseases, rendering autophagy a bone fide health-modifying agent. However, the clinical implication of canonical pan-autophagic activators or inhibitors has often led to undesirable side effects, making it urgent to find a safer autophagy-related therapeutic target. The discovery of non-canonical autophagic proteins has been found to specifically affect the development of diseases without causing a universal impact on autophagy and has shed light on finding a safer way to utilize autophagy in the therapeutic context. AREAS COVERED: This review summarizes recently discovered non-canonical autophagic proteins, how these proteins influence autophagy, and their potential therapeutic role in the disease due to their interaction with autophagy. EXPERT OPINION: Several therapies have been studied thus far and continued research is needed to identify the potential that non-canonical autophagic proteins have for treating certain diseases. In the meantime, continue to uncover new non-canonical autophagic proteins and examine which are likely to have therapeutic implications.