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Traumatic injury to the spinal cord causes permanent loss of function and major personal, social, and economic problems. Cell-based delivery strategies is a promising approach for treating spinal cord injury (SCI). However, the inhospitable microenvironment in the injured spinal cord results in poor cell survival and uncontrolled differentiation of the transplanted stem cells. The combination of a scaffold with cells has been developed with a tendency for achieving greater survival and integration with the host tissue. We investigated the effect of Matrigel combined with neural stem cells (NSCs) in vitro and in vivo . We compared the effect of different types of scaffold on the survival and differentiation of brain-derived NSCs in an in vitro culture. Subsequently, NSCs were transplanted subcutaneously into nude mice to detect graft survival and differentiation in vivo . Finally, phosphate-buffered saline (PBS), Matrigel alone, or Matrigel seeded with NSCs was injected into 48 subacute, clinically relevant rat models of SCI (16 rats per group). Matrigel supported cell survival and differentiation efficiently in vitro and in vivo . SCI rats transplanted with NSCs in Matrigel showed improved behavioral recovery and neuronal and reactive astrocyte marker expression levels compared to PBS- or Matrigel-transplanted rats. Functional repair and neuronal and reactive astrocyte marker expression was slightly improved in the Matrigel-alone group relative to the PBS group, but not statistically significantly. These data suggest that Matrigel is a promising scaffold material for cell transplantation to the injured spinal cord.

Severe injury occurs in the lung after acute spinal cord injury (ASCI) and autophagy is inhibited. However, rapamycin-activated autophagy's role and mechanism in lung injury development after ASCI is unknown. Preventing lung injury after ASCI by regulating autophagy is currently a valuable and unknown area. Herein, we aimed to investigate the effect and possible mechanism of rapamycin-activated autophagy on lung damage post-ASCI. An experimental animal study of rapamycin's effect and mechanism on lung damage after ASCI. We randomly divided 144 female wild-type Sprague–Dawley rats into a vehicle sham group (n = 36), a vehicle injury group (n = 36), a rapamycin sham group (n = 36), and a rapamycin injury group (n = 36). The spine was injured at the tenth thoracic vertebra using Allen's method. At 12, 24, 48, and 72 h after surgery, the rats were killed humanely. Lung damage was evaluated via pulmonary gross anatomy, lung pathology, and apoptosis assessment. Autophagy induction was assessed according to LC3, RAB7, and Beclin 1 levels. ULK-1, ULK-1 Ser555, ULK-1 Ser757, AMPK α and AMPK β1/2 were used to investigate the potential mechanism. After rapamycin pretreatment, the lung showed no obvious damage (e.g., cell death, inflammatory exudation, hemorrhage, and pulmonary congestion) at 12 h and 48 h after injury and Beclin1, LC3 and RAB7 levels increased. After rapamycin pretreatment, ULK-1, ULK-1 Ser555, and ULK-1 Ser757 levels increased at 12 h and 48 h after injury compared with the vehicle group, but they decreased at 12 h after injury compared with the rapamycin sham group. After rapamycin pretreatment, AMPKα levels did not change significantly before and after injury; however, at 48 h after injury, its level was elevated significantly compared with that in the vehicle group. Rapamycin can prevent lung injury after ASCI, possibly via upregulation of autophagy through the AMPK–mTORC1–ULK1 regulatory axis.

Background/Objectives: Acute spinal cord injury (ASCI) often leads to pulmonary complications, yet reliable, non-invasive assessment tools are limited. This study aimed to evaluate the utility of lung ultrasound (LUS) in assessing lung injury following ASCI in a rat model. Methods: Fifty-four female Sprague Dawley rats were randomized into sham (n = 27) or ASCI (n = 27) groups. LUS was performed at 12 h, 48 h, and 1 week post-injury, with lung injury quantified using a modified B-line score (BLS). Pulmonary function was assessed non-invasively, and histopathological evaluation and wet-to-dry (W/D) weight ratios were conducted post-mortem. Correlations between BLS and functional and pathological parameters were analyzed. Results: Histological analysis revealed progressive pulmonary hemorrhage, edema, and inflammatory infiltration peaking at 48 h post-injury, with residual hemorrhage and fibroplasia at 1 week. LUS findings evolved from narrow-based B-lines at 12 h to confluent B-lines with pleural abnormalities by 1 week. ASCI rats showed significant reductions in respiratory frequency, peak inspiratory and expiratory flow, and EF50 at all time points (p < 0.05). Tidal volume and minute volume decreased initially, with partial recovery at 1 week. BLS negatively correlated with all pulmonary function parameters and positively with the histological score and W/D ratio (p < 0.001). Conclusions: LUS reliably detects and tracks lung injury after ASCI, correlating well with physiological and pathological indicators. These findings support its potential as a non-invasive monitoring tool. Future refinement of ultrasound scoring may improve clinical applicability in ASCI-related pulmonary assessment.

Schwann cell (SC) transplantation is considered as a promising strategy for spinal cord injury. However, SCs show less capability in assisting the regenerative axons to penetrate through astrocyte (AS)-formed scar barrier. Anosmin-1, an extracellular matrix glycosylated adhesion protein expressed in the olfactory bulb, is involved in olfactory ensheathing cells and reborn olfactory nerve axons continually penetrating the glial barrier and targeting the olfactory bulb. In this study, we employ a dorsal root crush injury model treated with anosmin-1. A vertical climbing test was used for behavioral analysis and immunohistochemical study for SC/AS interaction in regenerative axon targeting. Anosmin-1 improved rat forepaw grasping as revealed by forelimb proprioception assessment. After treated with anosmin-1, p75+ immature SCs and P0+ mature SCs mingled well with ASs at the peripheral/central glial interface, reforming the glial barrier from a tight to loose structure. Furthermore, regenerated axons traced by BDA staining revealed proper axonal targeting to the dorsal horn of the spinal cord. These results suggest that anosmin-1 can regulate SC/AS interactions at the peripheral/central boundary site to open the glial barrier for regenerating axons crossing, targeting, and establishing functional neuronal circuits. Anosmin-1 might have a potential application in repair of spinal cord injuries, particularly in combination with SCs for autologous cell transplantation. Graphical Abstract

The Zhidan area in the Ordos Basin is enriched with unconventional hydrocarbon resources. However, there is a lack of research on oil–source correlation and specific petroleum secondary migration paths in the Chang 10 member, which restricts the exploration and development of petroleum resources in the area. Gas chromatography–mass spectrometry was used to determine the biomarker characteristics of 28 samples and the carbazole ratios of 14 oil sand extracts. Inductively coupled plasma–mass spectrometry was used to determine the rare earth element (REE) compositions of the oil sands and source rock extracts. Oil–source correlation analyses based on biomarkers and REE compositions were cross-validated, and the results showed that the Chang 10 oil in the study area originated from mixed source rocks of the Chang 7 and Chang 9 members, or from separate source rocks in either the Chang 7 or Chang 9 member. Based on the oil–source correlation, 11 secondary migration paths of Chang 10 oil were determined by applying carbazole parameters. The secondary migration path of oil shows that the Zhidan area's eastern part is the preferential oil accumulation area.

We introduce MinerU2.5, a 1.2B-parameter document parsing vision-language model that achieves state-of-the-art recognition accuracy while maintaining exceptional computational efficiency. Our approach employs a coarse-to-fine, two-stage parsing strategy that decouples global layout analysis from local content recognition. In the first stage, the model performs efficient layout analysis on downsampled images to identify structural elements, circumventing the computational overhead of processing high-resolution inputs. In the second stage, guided by the global layout, it performs targeted content recognition on native-resolution crops extracted from the original image, preserving fine-grained details in dense text, complex formulas, and tables. To support this strategy, we developed a comprehensive data engine that generates diverse, large-scale training corpora for both pretraining and fine-tuning. Ultimately, MinerU2.5 demonstrates strong document parsing ability, achieving state-of-the-art performance on multiple benchmarks, surpassing both general-purpose and domain-specific models across various recognition tasks, while maintaining significantly lower computational overhead.

This paper presents WanJuan-CC, a safe and high-quality open-sourced English webtext dataset derived from Common Crawl data. The study addresses the challenges of constructing large-scale pre-training datasets for language models, which require vast amounts of high-quality data. A comprehensive process was designed to handle Common Crawl data, including extraction, heuristic rule filtering, fuzzy deduplication, content safety filtering, and data quality filtering. From approximately 68 billion original English documents, we obtained 2.22T Tokens of safe data and selected 1.0T Tokens of high-quality data as part of WanJuan-CC. We have open-sourced 100B Tokens from this dataset. The paper also provides statistical information related to data quality, enabling users to select appropriate data according to their needs. To evaluate the quality and utility of the dataset, we trained 1B-parameter and 3B-parameter models using WanJuan-CC and another dataset, RefinedWeb. Results show that WanJuan-CC performs better on validation datasets and downstream tasks.

The evolution of Large Language Models (LLMs) like ChatGPT and GPT-4 has sparked discussions on the advent of Artificial General Intelligence (AGI). However, replicating such advancements in open-source models has been challenging. This paper introduces InternLM2, an open-source LLM that outperforms its predecessors in comprehensive evaluations across 6 dimensions and 30 benchmarks, long-context modeling, and open-ended subjective evaluations through innovative pre-training and optimization techniques. The pre-training process of InternLM2 is meticulously detailed, highlighting the preparation of diverse data types including text, code, and long-context data. InternLM2 efficiently captures long-term dependencies, initially trained on 4k tokens before advancing to 32k tokens in pre-training and fine-tuning stages, exhibiting remarkable performance on the 200k ``Needle-in-a-Haystack\" test. InternLM2 is further aligned using Supervised Fine-Tuning (SFT) and a novel Conditional Online Reinforcement Learning from Human Feedback (COOL RLHF) strategy that addresses conflicting human preferences and reward hacking. By releasing InternLM2 models in different training stages and model sizes, we provide the community with insights into the model's evolution.