Isolated theta waves originating from the midline thalamus trigger memory reactivation during NREM sleep in mice

During non-rapid eye movement (NREM) sleep, neural ensembles in the entorhinal-hippocampal circuit responsible for encoding recent memories undergo reactivation to facilitate the process of memory consolidation. This reactivation is widely acknowledged as pivotal for the formation of stable memory and its impairment is closely associated with memory dysfunction. To date, the neural mechanisms driving the reactivation of neural ensembles during NREM sleep remain poorly understood. Here, we show that the neural ensembles in the medial entorhinal cortex (MEC) that encode spatial experiences exhibit reactivation during NREM sleep. Notably, this reactivation consistently coincides with isolated theta waves. In addition, we found that the nucleus reuniens (RE) in the midline thalamus exhibits typical theta waves during NREM sleep, which are highly synchronized with those occurring in the MEC in male mice. Closed-loop optogenetic inhibition of the RE-MEC pathway specifically suppressed these isolated theta waves, resulting in impaired reactivation and compromised memory consolidation following a spatial memory task in male mice. The findings suggest that theta waves originating from the ventral midline thalamus play a role in initiating memory reactivation and consolidation during sleep.

Engineering an Epoxide Hydrolase for Chemoenzymatic Asymmetric Synthesis of Chiral Triazole Fungicide (S)- and (R)-Flutriafol

Flutriafol, a globally utilized triazole fungicide in agriculture, is typically applied as a racemic mixture, but its enantiomers differ in bioactivity and environmental impact. The synthesis of flutriafol enantiomers is critically dependent on chiral precursors: 2,2-bisaryl-substituted oxirane [(2-fluorophenyl)-2-(4-fluorophenyl)oxirane, 1a] and 1,2-diol [1-(2-fluorophenyl)-1-(4-fluorophenyl)ethane-1,2-diol, 1b]. Here, we engineered a Rhodotorula paludigensis epoxide hydrolase (RpEH), obtaining mutant Escherichia coli/RpehH336W/L360F with a 6.4-fold enhanced enantiomeric ratio (E) from 5.5 to 35.4. This enabled a gram-scale resolution of rac-1a by E. coli/RpehH336W/L360F, producing (S)-1a (98.2% ees) and (R)-1b (75.0% eep) with 44.3 and 55.7% analytical yields, respectively. As follows, chiral (S)-flutriafol (98.2% ee) and (R)-flutriafol (75.0% ee) were easily synthesized by a one-step chemocatalytic process from (S)-1a and a two-step chemocatalytic process from (R)-1b, respectively. This chemoenzymatic approach offers a superior alternative for the asymmetric synthesis of flutriafol enantiomers. Furthermore, molecular dynamics simulations revealed insight into the enantioselectivity improvement of RpEH toward bulky 2,2-bisaryl-substituted oxirane 1a.

Altered expression of the Plexin-B2 system in tuberous sclerosis complex and focal cortical dysplasia IIb lesions

Tuberous sclerosis complex (TSC) and focal cortical dysplasia (FCD) type IIb are the predominant causes of drug-refractory epilepsy in children. Dysmorphic neurons (DNs), giant cells (GCs), and balloon cells (BCs) are the most typical pathogenic profiles in cortical lesions of TSC and FCD IIb patients. However, mechanisms underlying the pathological processes of TSC and FCD IIb remain obscure. The Plexin-B2-Sema4C signalling pathway plays critical roles in neuronal morphogenesis and corticogenesis during the development of the central nervous system. However, the role of the Plexin-B2 system in the pathogenic process of TSC and FCD IIb has not been identified. In the present study, we investigated the expression and cell distribution characteristics of Plexin-B2 and Sema4C in TSC and FCD IIb lesions with molecular technologies. Our results showed that the mRNA and protein levels of Plexin-B2 expression were significantly increased both in TSC and FCD IIb lesions versus that in the control cortex. Notably, Plexin-B2 was also predominantly observed in GCs in TSC epileptic lesions and BCs in FCD IIb lesions. In contrast, the expression of Sema4C, the ligand of Plexin-B2, was significantly decreased in DNs, GCs, and BCs in TSC and FCD IIb epileptic lesions. Additionally, Plexin-B2 and Sema4C were expressed in astrocytes and microglia cells in TSC and FCD IIb lesions. Furthermore, the expression of Plexin-B2 was positively correlated with seizure frequency in TSC and FCD IIb patients. In conclusion, our results showed the Plexin-B2-Sema4C system was abnormally expressed in cortical lesions of TSC and FCD IIb patients, signifying that the Plexin-B2-Sema4C system may play a role in the pathogenic development of TSC and FCD IIb.

Regulatory mechanisms of amino acids in ferroptosis

Ferroptosis, an iron-dependent non-apoptotic regulated cell death process, is associated with the pathogenesis of various diseases. Amino acids, which are indispensable substrates of vital activities, significantly regulate ferroptosis. Amino acid metabolism is involved in maintaining iron and lipid homeostasis and redox balance. The regulatory effects of amino acids on ferroptosis are complex. An amino acid may exert contrasting effects on ferroptosis depending on the context. This review systematically and comprehensively summarized the distinct roles of amino acids in regulating ferroptosis and highlighted the emerging opportunities to develop clinical therapeutic strategies targeting amino acid-mediated ferroptosis.

Hallmarks of perineural invasion in pancreatic ductal adenocarcinoma: new biological dimensions

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive malignant tumor with a high metastatic potential. Perineural invasion (PNI) occurs in the early stages of PDAC with a high incidence rate and is directly associated with a poor prognosis. It involves close interaction among PDAC cells, nerves and the tumor microenvironment. In this review, we detailed discuss PNI-related pain, six specific steps of PNI, and treatment of PDAC with PNI and emphasize the importance of novel technologies for further investigation.

Climbing Fiber Activation Induced by Footshock in the Cerebellar Vermis Lobule IV/V of Freely Moving Mice

Parallel fibers (PFs) in the cerebellar cortex are involved in a series of coordinated responses in the fear conditioning paradigm induced by footshock. However, whether footshock can activate cerebellar climbing fibers (CFs) remains unclear. In this study, we recorded calcium (Ca2+) activity in CFs by optical fiber photometry in the cerebellar vermis lobule IV/V of freely moving mice with footshock stimulation. We found that the activation of CFs in the lobule IV/V was highly correlated with footshock stimulation but not with the sound stimulation used as a control. This result suggests that afferent information from CFs might be associated with the motor initiation of fear-related behaviors or fear emotion itself. Thus, our results suggest that a characteristic CF signal in the cerebellar cortex might be related to fear processing or footshock-related behaviors (such as startle responses or pain sensation).

Global Trends in Orthopedic Biofilm Research: A Bibliometric Analysis of 1994-2022

Objective

Bibliometric analysis is commonly used to visualize the knowledge foundation, trends, and patterns in a specific scientific field by performing a quantitative evaluation of the relevant literature. The purpose of this study was to perform a bibliometric analysis of recent studies in the field of orthopedic biofilm research and identify its current trends and hotspots.

Methods

Research studies were retrieved from the Web of Science Core Collection and Scopus databases and analyzed in bibliometrix with R package (4.2.2).

Results

A total of 2426 literature were included in the study. Journal of orthopaedic research and Clinical orthopaedics and related research ranked first in terms of productivity and impact, with 57 published articles and 32 h-index, respectively. Trampuz A, Ohio State Univ and the United States ranked as the most productive authors, institutions, and countries. Biofilm formation, role of sonication, biomaterial mechanism and antibiotic loading have been investigated as the trend and hotspots in the field of orthopedic biofilm research.

Conclusion

This study provides a thorough overview of the state of the art of current orthopedic biofilm research and offers valuable insights into recent trends and hotspots in this field.

Automatic segmentation of dura for quantitative analysis of lumbar stenosis: A deep learning study with 518 CT myelograms

BACKGROUND

The diagnosis of lumbar spinal stenosis (LSS) can be challenging because radicular pain is not often present in the culprit-level localization. Accurate segmentation and quantitative analysis of the lumbar dura on radiographic images are key to the accurate differential diagnosis of LSS. The aim of this study is to develop an automatic dura-contouring tool for radiographic quantification on computed tomography myelogram (CTM) for patients with LSS.

METHODS

A total of 518 CTM cases with or without lumbar stenosis were included in this study. A deep learning (DL) segmentation algorithm 3-dimensional (3D) U-Net was deployed. A total of 210 labeled cases were used to develop the dura-contouring tool, with the ratio of the training, independent testing, and external validation datasets being 150:30:30. The Dice score (DCS) was the primary measure to evaluate the segmentation performance of the 3D U-Net, which was subsequently developed as the dura-contouring tool to segment another unlabeled 308 CTM cases with LSS. Automatic masks of 446 slices on the stenotic levels were then meticulously reviewed and revised by human experts, and the cross-sectional area (CSA) of the dura was compared.

RESULTS

The mean DCS of the 3D U-Net were 0.905 ± 0.080, 0.933 ± 0.018, and 0.928 ± 0.034 in the five-fold cross-validation, the independent testing, and the external validation datasets, respectively. The segmentation performance of the dura-contouring tool was also comparable to that of the second observer (the human expert). With the dura-contouring tool, only 59.0% (263/446) of the automatic masks of the stenotic slices needed to be revised. In the revised cases, there were no significant differences in the dura CSA between automatic masks and corresponding revised masks (p = 0.652). Additionally, a strong correlation of dura CSA was found between the automatic masks and corresponding revised masks (r = 0.805).

CONCLUSIONS

A dura-contouring tool was developed that could automatically segment the dural sac on CTM, and it demonstrated high accuracy and generalization ability. Additionally, the dura-contouring tool has the potential to be applied in patients with LSS because it facilitates the quantification of the dural CSA on stenotic slices.

[Interpretation of the reporting specifications for mixed methods systematic reviews]

The number of mixed methods systematic reviews (MMSRs) published internationally is increasing day by day, thanks to the continuous development and improvement of MMSRs methodological guidelines and reporting specification, which effectively promote the depth and breadth of evidence synthesis and integration results. However, the application of this method has yet to be popularized in China. With the continuous development of mixed methods research and evidence-based medicine in our country, the number of MMSRs will gradually increase. This paper aims to analyze the reporting specifications for MMSRs with cases to improve the quality of evidence integration and reporting standardization of domestic relevant researchers in MMSRs.

Early Prognostication of Critical Patients With Spinal Cord Injury: A Machine Learning Study With 1485 Cases

STUDY DESIGN

A retrospective case-series.

OBJECTIVE

The study aims to use machine learning to predict the discharge destination of spinal cord injury (SCI) patients in the intensive care unit.

SUMMARY OF BACKGROUND DATA

Prognostication following SCI is vital, especially for critical patients who need intensive care.

PATIENTS AND METHODS

Clinical data of patients diagnosed with SCI were extracted from a publicly available intensive care unit database. The first recorded data of the included patients were used to develop a total of 98 machine learning classifiers, seeking to predict discharge destination (eg, death, further medical care, home, etc.). The microaverage area under the curve (AUC) was the main indicator to assess discrimination. The best average-AUC classifier and the best death-sensitivity classifier were integrated into an ensemble classifier. The discrimination of the ensemble classifier was compared with top death-sensitivity classifiers and top average-AUC classifiers. In addition, prediction consistency and clinical utility were also assessed.

RESULTS

A total of 1485 SCI patients were included. The ensemble classifier had a microaverage AUC of 0.851, which was only slightly inferior to the best average-AUC classifier ( P =0.10). The best average-AUC classifier death sensitivity was much lower than that of the ensemble classifier. The ensemble classifier had a death sensitivity of 0.452, which was inferior to the top 8 death-sensitivity classifiers, whose microaverage AUC were inferior to the ensemble classifier ( P <0.05). In addition, the ensemble classifier demonstrated a comparable Brier score and superior net benefit in the DCA when compared with the performance of the origin classifiers.

CONCLUSIONS

The ensemble classifier shows an overall superior performance in predicting discharge destination, considering discrimination ability, prediction consistency, and clinical utility. This classifier system may aid in the clinical management of critical SCI patients in the early phase following injury.

LEVEL OF EVIDENCE

Level 3.

The effect of childhood harshness and unpredictability on Internet addiction among college students: The mediating effect of self-control

College students are inevitably online and at risk of becoming addicted. Life history theory provides an explanatory framework for individual differences in Internet addiction, and childhood harshness and unpredictability may be important antecedents. However, it is unclear whether and how childhood harshness and/or unpredictability affect Internet addiction during college. In this study, we recruited 483 Chinese college students and assessed their childhood harshness, unpredictability, self-control, and Internet addiction. The results of path analysis showed that childhood unpredictability was positively associated with Internet addiction among college students and was partially mediated by self-control. The effect of harshness on Internet addiction showed a suppression effect, i.e., the direct effect of harshness on Internet addiction was negative and the indirect effect through self-control was positive. This suggests that the high risk of Internet addiction stems from harshness and unpredictability in childhood, but that the effects of these factors are independent and distinct. Self-control plays an important role in this process, but many internal mechanisms remain to be tested in future research.

STPoseNet: A real-time spatiotemporal network model for robust mouse pose estimation

Animal behavior analysis plays a crucial role in contemporary neuroscience research. However, the performance of the frame-by-frame approach may degrade in scenarios with occlusions or motion blur. In this study, we propose a spatiotemporal network model based on YOLOv8 to enhance the accuracy of key-point detection in mouse behavioral experimental videos. This model integrates a time-domain tracking strategy comprising two components: the first part utilizes key-point detection results from the previous frame to detect potential target locations in the subsequent frame; the second part employs Kalman filtering to analyze key-point changes prior to detection, allowing for the estimation of missing key-points. In the comparison of pose estimation results between our approach, YOLOv8, DeepLabCut and SLEAP on videos of three mouse behavioral experiments, our approach demonstrated significantly superior performance. This suggests that our method offers a new and effective means of accurately tracking and estimating pose in mice through spatiotemporal processing.

Antibacterial and self-healing sepiolite-based hybrid hydrogel for hemostasis and wound healing

The process of wound healing necessitates a specific environment, thus prompting extensive research into the utilization of hydrogels for this purpose. While numerous hydrogel structures have been investigated, the discovery of a self-healing hydrogel possessing favorable biocompatibility, exceptional mechanical properties, and effective hemostatic and antibacterial performance remains uncommon. In this work, a polyvinyl alcohol (PVA) hybrid hydrogel was meticulously designed through a simple reaction, wherein CuxO anchored sepiolite was incorporated into the hydrogel. The results indicate that introduction of sepiolite greatly improves the toughness, self-healing and adhesion properties of the PVA hydrogels. CuxO nanoparticles endow the hydrogels with excellent antibacterial performance towards Staphylococcus aureus and Escherichia coli. The application of hybrid hydrogels for fast hemostasis and wound healing are verified in vitro and in vivo with rat experiments. This work thereby demonstrates an effective strategy for designing biodegradable hemostatic and wound healing materials.

NeuroSeg-III: efficient neuron segmentation in two-photon Ca2+ imaging data using self-supervised learning

Two-photon Ca2+ imaging technology increasingly plays an essential role in neuroscience research. However, the requirement for extensive professional annotation poses a significant challenge to improving the performance of neuron segmentation models. Here, we present NeuroSeg-III, an innovative self-supervised learning approach specifically designed to achieve fast and precise segmentation of neurons in imaging data. This approach consists of two modules: a self-supervised pre-training network and a segmentation network. After pre-training the encoder of the segmentation network via a self-supervised learning method without any annotated data, we only need to fine-tune the segmentation network with a small amount of annotated data. The segmentation network is designed with YOLOv8s, FasterNet, efficient multi-scale attention mechanism (EMA), and bi-directional feature pyramid network (BiFPN), which enhanced the model's segmentation accuracy while reducing the computational cost and parameters. The generalization of our approach was validated across different Ca2+ indicators and scales of imaging data. Significantly, the proposed neuron segmentation approach exhibits exceptional speed and accuracy, surpassing the current state-of-the-art benchmarks when evaluated using a publicly available dataset. The results underscore the effectiveness of NeuroSeg-III, with employing an efficient training strategy tailored for two-photon Ca2+ imaging data and delivering remarkable precision in neuron segmentation.

Machine Learning Predict Survivals of Spinal and Pelvic Ewing's Sarcoma with the SEER Database

STUDY DESIGN

Retrospective Cohort Study.

OBJECTIVES

This study aimed to develop survival prediction models for spinal Ewing's sarcoma (EWS) based on machine learning (ML).

METHODS

We extracted the SEER registry's clinical data of EWS diagnosed between 1975 and 2016. Three feature selection methods extracted clinical features. Four ML algorithms (Cox, random survival forest (RSF), CoxBoost, DeepCox) were trained to predict the overall survival (OS) and cancer-specific survival (CSS) of spinal EWS. The concordance index (C-index), integrated Brier score (IBS) and mean area under the curves (AUC) were used to assess the prediction performance of different ML models. The top initial ML models with best performance from each evaluation index (C-index, IBS and mean AUC) were finally stacked to ensemble models which were compared with the traditional TNM stage model by 3-/5-/10-year Receiver Operating Characteristic (ROC) curves and Decision Curve Analysis (DCA).

RESULTS

A total of 741 patients with spinal EWS were identified. C-index, IBS and mean AUC for the final ensemble ML model in predicting OS were .693/0.158/0.829 during independent testing, while .719/0.171/0.819 in predicting CSS. The ensemble ML model also achieved an AUC of .705/0.747/0.851 for predicting 3-/5-/10-year OS during independent testing, while .734/0.779/0.830 for predicting 3-/5-/10-year CSS, both of which outperformed the traditional TNM stage. DCA curves also showed the advantages of the ensemble models over the traditional TNM stage.

CONCLUSION

ML was an effective and promising technique in predicting survival of spinal EWS, and the ensemble models were superior to the traditional TNM stage model.

[Research progress on ferroptosis in tumor immunity]

Ferroptosis is a novel form of cell death that is induced by excessive accumulation of ferrous ions and lipid peroxides. It triggers the release of damage-associated molecular patterns through autophagy-dependent mechanisms, serving as an adjunct to immunogenic cell death and activating both adaptive and innate immunity. In the tumor microenvironment, the regulation and influence of tumor cells and immune cells undergoing ferroptosis are regulated by various factors, which plays a crucial role in tumor development, treatment, and prognosis. This article provides an overview of the biological effects of ferroptosis on immune cells such as T cells, macrophages, neutrophils and B cells and tumor cells in the tumor microenvironment.

Mapping the hotspots and future trends of electrical stimulation for peripheral nerve injury: A bibliometric analysis from 2002 to 2023

Peripheral nerve injuries often result in severe personal and social burden, and even with surgical treatment, patients continue to have poor clinical outcomes. Over the past two decades, electrical stimulation has been shown to promote axonal regeneration and alleviate refractory neuropathic pain. The aim of this study was to analyse this field using a bibliometric approach. Literature was searched through Web of Science Core Collection (WOSCC) for the years 2002-2023. Literature analysis included: (1) Describing publication trends in the field. (2) Exploring collaborative network relationships. (3) Finding research advances and research hotspots in the field. (4) Summarizing research trends in the field. With the number of studies in this field still increasing, a total of 693 publications were included in the analysis. This field of research is interdisciplinary in nature. Research hotspots include peripheral nerve regeneration, the treatment of neuropathic pain, materials for nerve injury repair, and the restoration of sensory function in patients with peripheral nerve injury. Correspondingly, the development of nerve conduits and systems for peripheral nerve electrical stimulation, clinical trials of peripheral nerve electrical stimulation, and tactile recovery and movement for amputees have shown significant promise as future research trends in this field.

BNST GABAergic neurons modulate wakefulness over sleep and anesthesia

The neural circuits underlying sleep-wakefulness and general anesthesia have not been fully investigated. The GABAergic neurons in the bed nucleus of the stria terminalis (BNST) play a critical role in stress and fear that relied on heightened arousal. Nevertheless, it remains unclear whether BNST GABAergic neurons are involved in the regulation of sleep-wakefulness and anesthesia. Here, using in vivo fiber photometry combined with electroencephalography, electromyography, and video recordings, we found that BNST GABAergic neurons exhibited arousal-state-dependent alterations, with high activities in both wakefulness and rapid-eye movement sleep, but suppressed during anesthesia. Optogenetic activation of these neurons could initiate and maintain wakefulness, and even induce arousal from anesthesia. However, chronic lesion of BNST GABAergic neurons altered spontaneous sleep-wakefulness architecture during the dark phase, but not induction and emergence from anesthesia. Furthermore, we also discovered that the BNST-ventral tegmental area pathway might participate in promoting wakefulness and reanimation from steady-state anesthesia. Collectively, our study explores new elements in neural circuit mechanisms underlying sleep-wakefulness and anesthesia, which may contribute to a more comprehensive understanding of consciousness and the development of innovative anesthetics.

Many-objective bi-level energy scheduling method for integrated energy stations based on power allocation strategy

The integrated energy station of new energy vehicle hydrogenation/charging/power exchange is proposed, which also includes hydrogen production, hydrogen storage, electricity sales to users and the grid (WPIES). To address the efficiency of renewable energy use, this paper proposes a future value competition strategy for wind and photovoltaic (PV) allocation based on goal optimization (FVCS). In order to better realize the distribution of wind power/PV in the integrated energy station and improve the energy utilization efficiency of the integrated energy station, a two-layer optimization model of FVCS-WPIES is proposed, in which the upper layer model aims to maximize the expected income. The goals of the lower-level model are to maximize total profit, minimize battery losses, and minimize pollutant emissions. The model also considers the hydrogen power constraint and the upper-level model penalty. The comparison results show that the Pareto solution set is superior to the traditional model.

[Role of mitochondrial autophagy and the curative effect of rehmannia glutinosa leaves total glycoside capsules on nucleos(t)ide drug-induced renal injury]

Objective: To study the curative effect of rehmannia glutinosa leaves total glycoside capsules and the role of mitochondrial autophagy on nucleos(t)ide drug-induced renal injury. Methods: Adefovir dipivoxil (ADV) was used to construct a hepatitis B virus (HBV) transgenic mouse model for renal injury. Renal function was measured in each group at one and two weeks of modeling. Mitochondrial autophagy indicators were measured at two weeks of modeling in renal tissue. Transmission electron microscopy was used to detect mitochondrial autophagy phenomena in renal tissue. The model was established for two weeks. Mouse with renal injury were treated with rehmannia glutinosa leaves total glycoside capsules or isotonic saline for eight weeks by intragastric administration. Renal function was measured. Renal tissue morphology was observed. Mitochondrial autophagy indicators were detected in renal tissue. The protective effect of different concentrations of verbascoside (the main active ingredient of rehmannia glutinosa capsule) was observed on HK-2 cell damage induced by ADV. HK-2 cells were divided into control, ADV, and ADV plus verbascoside groups. The effects of verbascoside at different times and concentrations were observed on the HK-2 mitochondrial autophagy indicators. Fifty patients with chronic hepatitis B were collected who presented with renal injury after treatment with nucleos(t)ide analogs. The random number method was used to divide 29 cases into a control group that received conventional treatment. The treatment group of 21 cases was treated with rehmannia glutinosa leaves total glycoside capsules on the basis of the control group. Serum creatinine (Scr) and urinary protein were detected at eight weeks.The χ(2) test or t-test was used for statistical analysis. Results: Compared with the control group, two weeks of modeling in the ADV group induced renal function injury in HBV mice. The expression of autophagy indicators was higher in the renal tissue of the ADV group than that of the control group. Transmission electron microscopy had revealed mitochondrial autophagy in the renal tissue of the ADV group. Compared with the control group, the renal function of HBV mice treated with rehmannia glutinosa leaves total glycoside capsules improved for two months, and the expressions of autophagy indicators were down-regulated.Verbascoside promoted proliferation in ADV-damaged HK-2 cells, and the expression of autophagy indicators was down-regulated compared with the ADV alone group. In 50 patients with renal function injury, the urinary protein improvement was significantly superior in the treatment group than that in the control group, with eighteen and three cases being effective and ineffective in the treatment group and 12 and 17 cases being effective and ineffective in the control group, with a statistically significant difference (χ(2) = 9.975 0, P = 0.001 6). Serum creatinine was decreased in the treatment group compared with the control group, with 11 and 10 cases being effective and ineffective in the treatment group and 12 and 17 cases being effective and ineffective in the control group, with no statistically significant difference (χ(2) = 0.593 5, P = 0.441 1). Conclusion: Rehmannia glutinosa leaves total glycoside capsule can improve the nucleos(t)ide drug-induced renal function injury in chronic hepatitis B, possibly playing a role via inhibiting PINK1/Parkin-mediated mitochondrial autophagy.

Phase I study of peposertib and avelumab with or without palliative radiotherapy in patients with advanced solid tumors

INTRODUCTION

We report results from a phase I, three-part, dose-escalation study of peposertib, a DNA-dependent protein kinase inhibitor, in combination with avelumab, an immune checkpoint inhibitor, with or without radiotherapy in patients with advanced solid tumors.

MATERIALS AND METHODS

Peposertib 100-400 mg twice daily (b.i.d.) or 100-250 mg once daily (q.d.) was administered in combination with avelumab 800 mg every 2 weeks in Part A or avelumab plus radiotherapy (3 Gy/fraction × 10 days) in Part B. Part FE assessed the effect of food on the pharmacokinetics of peposertib plus avelumab. The primary endpoint in Parts A and B was dose-limiting toxicity (DLT). Secondary endpoints were safety, best overall response per RECIST version 1.1, and pharmacokinetics. The recommended phase II dose (RP2D) and maximum tolerated dose (MTD) were determined in Parts A and B.

RESULTS

In Part A, peposertib doses administered were 100 mg (n = 4), 200 mg (n = 11), 250 mg (n = 4), 300 mg (n = 6), and 400 mg (n = 4) b.i.d. Of DLT-evaluable patients, one each had DLT at the 250-mg and 300-mg dose levels and three had DLT at the 400-mg b.i.d. dose level. In Part B, peposertib doses administered were 100 mg (n = 3), 150 mg (n = 3), 200 mg (n = 4), and 250 mg (n = 9) q.d.; no DLT was reported in evaluable patients. Peposertib 200 mg b.i.d. plus avelumab and peposertib 250 mg q.d. plus avelumab and radiotherapy were declared as the RP2D/MTD. No objective responses were observed in Part A or B; one patient had a partial response in Part FE. Peposertib exposure was generally dose proportional.

CONCLUSIONS

Peposertib doses up to 200 mg b.i.d. in combination with avelumab and up to 250 mg q.d. in combination with avelumab and radiotherapy were tolerable in patients with advanced solid tumors; however, antitumor activity was limited.

CLINICALTRIALS

GOV IDENTIFIER

NCT03724890.

A Suture Technique for Ruptured Annulus Fibrosus Following Decompression Under Percutaneous Transforaminal Endoscopic Discectomy

The suture technique for a ruptured annulus fibrosus (AF) under full-endoscopy remains challenging. Direct suturing of a ruptured annular tear after full decompression has been shown to decrease the recurrence rate of lumbar disc herniation during endoscopic surgery. Traditional suture operations under endoscopy involve only simple suturing of the ruptured AF. Due to the weak and poor quality of the AF tissue around the tear portal, using this area as needle insertion points during suturing may lead to insufficient tension and a low success rate of AF closure. Currently, there is no detailed technical illustration based on video for AF tear suturing under lumbar full-endoscopy. We innovatively propose a method of covering and suturing the AF tear by pulling up the posterior longitudinal ligament (PLL) under lumbar endoscopy and using three stitches (PLL-AF suture technique). The patient who received the novel suture technique achieved satisfactory results. Six months after the operation, lumbar MRI showed no evidence of recurrence in the outpatient clinic.

Artificial intelligence applications in bone fractures: A bibliometric and science mapping analysis

Background

Bone fractures are a common medical issue worldwide, causing a serious economic burden on society. In recent years, the application of artificial intelligence (AI) in the field of fracture has developed rapidly, especially in fracture diagnosis, where AI has shown significant capabilities comparable to those of professional orthopedic surgeons. This study aimed to review the development process and applications of AI in the field of fracture using bibliometric analysis, while analyzing the research hotspots and future trends in the field.

Materials and methods

Studies on AI and fracture were retrieved from the Web of Science Core Collections since 1990, a retrospective bibliometric and visualized study of the filtered data was conducted through CiteSpace and Bibliometrix R package.

Results

A total of 1063 publications were included in the analysis, with the annual publication rapidly growing since 2017. China had the most publications, and the United States had the most citations. Technical University of Munich, Germany, had the most publications. Doornberg JN was the most productive author. Most research in this field was published in Scientific Reports. Doi K's 2007 review in Computerized Medical Imaging and Graphics was the most influential paper.

Conclusion

AI application in fracture has achieved outstanding results and will continue to progress. In this study, we used a bibliometric analysis to assist researchers in understanding the basic knowledge structure, research hotspots, and future trends in this field, to further promote the development of AI applications in fracture.

Machine Learning Predicts Decompression Levels for Lumbar Spinal Stenosis Using Canal Radiomic Features from Computed Tomography Myelography

BACKGROUND

The accurate preoperative identification of decompression levels is crucial for the success of surgery in patients with multi-level lumbar spinal stenosis (LSS). The objective of this study was to develop machine learning (ML) classifiers that can predict decompression levels using computed tomography myelography (CTM) data from LSS patients.

METHODS

A total of 1095 lumbar levels from 219 patients were included in this study. The bony spinal canal in CTM images was manually delineated, and radiomic features were extracted. The extracted data were randomly divided into training and testing datasets (8:2). Six feature selection methods combined with 12 ML algorithms were employed, resulting in a total of 72 ML classifiers. The main evaluation indicator for all classifiers was the area under the curve of the receiver operating characteristic (ROC-AUC), with the precision-recall AUC (PR-AUC) serving as the secondary indicator. The prediction outcome of ML classifiers was decompression level or not.

RESULTS

The embedding linear support vector (embeddingLSVC) was the optimal feature selection method. The feature importance analysis revealed the top 5 important features of the 15 radiomic predictors, which included 2 texture features, 2 first-order intensity features, and 1 shape feature. Except for shape features, these features might be eye-discernible but hardly quantified. The top two ML classifiers were embeddingLSVC combined with support vector machine (EmbeddingLSVC_SVM) and embeddingLSVC combined with gradient boosting (EmbeddingLSVC_GradientBoost). These classifiers achieved ROC-AUCs over 0.90 and PR-AUCs over 0.80 in independent testing among the 72 classifiers. Further comparisons indicated that EmbeddingLSVC_SVM appeared to be the optimal classifier, demonstrating superior discrimination ability, slight advantages in the Brier scores on the calibration curve, and Net benefits on the Decision Curve Analysis.

CONCLUSIONS

ML successfully extracted valuable and interpretable radiomic features from the spinal canal using CTM images, and accurately predicted decompression levels for LSS patients. The EmbeddingLSVC_SVM classifier has the potential to assist surgical decision making in clinical practice, as it showed high discrimination, advantageous calibration, and competitive utility in selecting decompression levels in LSS patients using canal radiomic features from CTM.