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Stand basal area (SBA) is an important variable in the prediction of forest growth and harvest yield. However, achieving the additivity of SBA models for multiple tree species in the complex structure of broad-leaved mixed forests is an urgent scientific issue in the study of accurately predicting the SBA of mixed forests. This study used data from 58 sample plots (30 m × 30 m) for Populus davidiana × Betula platyphylla broad-leaved mixed forests to construct the SBA basic model based on nonlinear least squares regression (NLS). Adjustment in proportion (AP) and nonlinear seemingly unrelated regression (NSUR) were used to construct a multi-species additive basal area prediction model. The results identified the Richards model (M6) and Korf model (M1) as optimal for predicting the SBA of P. davidiana and B. platyphylla, respectively. The SBA models incorporate site quality, stand density index, and age at 1.3 m above ground level, which improves the prediction accuracy of basal area. Compared to AP, NSUR is an effective method for addressing the additivity of basal area in multi-species mixed forests. The results of this study can provide a scientific basis for optimizing stand structure and accurately predicting SBA in multi-species mixed forests. [ABSTRACT FROM AUTHOR]

Objective: Imaging parameters of Chiari malformation type I (CMI) development are not well established. This study aimed to collect evidence of general or specific imaging measurements in patients with CMI, analyze indicators that may assist in determining the severity of CMI, and guide its diagnosis and treatment. Methods: A comprehensive search was conducted across various databases including the Cochrane Library, PubMed, MEDLINE, Scopus, and Embase, covering the period from January 2002 to October 2023, following predefined inclusion criteria. Meta-analyses were performed using RevMan (ver. 5.4). We performed a quantitative summary and systematic analysis of the included studies. This study was registered in the PROSPERO (International Prospective Register of Systematic Reviews) prior to initiation (CRD42023415454). Results: Thirty-three studies met our inclusion criteria. The findings indicated that out of the 14 parameters examined, 6 (clivus length, basal angle, Boogard's angle, supraocciput lengths, posterior cranial fossa [PCF] height, and volume) exhibited significant differences between the CMI group and the control group. Furthermore, apart from certain anatomical parameters that hold prognostic value for CMI, functional parameters like tonsillar movement, obex displacement, and cerebrospinal fluid dynamics serve as valuable indicators for guiding the clinical management of the disease. Conclusion: We collated and established a set of linear, angular, and area measurements deemed essential for diagnosing CMI. However, more indicators can only be analyzed descriptively for various reasons, particularly in prognostic prediction. We posit that the systematic assessment of patients' PCF morphology, volume, and other parameters at a 3-dimensional level holds promising clinical application prospects. [ABSTRACT FROM AUTHOR]

This study aimed at identifying reliable differentially expressed miRNAs (DEMs) for schizophrenia in blood via meta-analyses combined with deep learning methods. First, we meta-analysed published DEMs. Then, we enriched the pool of schizophrenia-associated miRNAs by applying two computational learning methods to identify candidate biomarkers and verified the results in external datasets. In total, 27 DEMs were found to be statistically significant (p <.05). Ten candidate schizophrenia-associated miRNAs were identified through computational learning methods. The diagnostic efficiency was verified on a blood-miRNA dataset (GSE54578) with a random forest (RF) model and achieved an area under the curve (AUC) of 0.83 ± 0.14. Moreover, 855 experimentally validated target genes for these candidate miRNAs were retrieved, and 11 hub genes were identified. Enrichment analysis revealed that the main functions in which the target genes were enriched were those related to cell signalling, prenatal infections, cancers, cell deaths, oxidative stress, endocrine disorders, transcription regulation, and kinase activities. The diagnostic ability of the hub genes was reflected in a comparably good average AUC of 0.77 ± 0.09 for an external dataset (GSE38484). A meta-analysis that combines computational and mathematical methods provides a reliable tool for identifying candidate biomarkers of schizophrenia. [ABSTRACT FROM AUTHOR]

Lumbar spine disorders often cause lower back pain, lower limb radiating pain, restricted movement, and neurological dysfunction, which seriously affect the quality of life of middle-aged and older people. It has been found that pathological changes in the spine often cause changes in the morphology and function of the paraspinal muscles (PSMs). Fatty infiltration (FI) in PSMs is closely associated with disc degeneration and Modic changes. And FI causes inflammatory responses that exacerbate the progression of lumbar spine disease and disrupt postoperative recovery. Magnetic resonance imaging can better distinguish between fat and muscle tissue with the threshold technique. Three-dimensional magnetic resonance imaging multi-echo imaging techniques such as water-fat separation and proton density are currently popular for studying FI. Muscle fat content obtained based on these imaging sequences has greater accuracy, visualization, acquisition speed, and utility. The proton density fat fraction calculated from these techniques has been shown to evaluate more subtle changes in PSMs. Magnetic resonance spectroscopy can accurately reflect the relationship between FI and the degeneration of PSMs by measuring intracellular and extracellular lipid values to quantify muscle fat. We have pooled and analyzed published studies and found that patients with spinal disorders often exhibit FI in PSMs. Some studies suggest an association between FI and adverse surgical outcomes, although conflicting results exist. These suggest that clinicians should consider FI when assessing surgical risks and outcomes. Future studies should focus on understanding the biological mechanisms underlying FI and its predictive value in spinal surgery, providing valuable insights for clinical decision-making., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Mapping the localization of the functional brain regions in trigeminal neuralgia (TN) patients is still lacking. The study aimed to explore the functional brain alterations and influencing factors in TN patients using functional brain imaging techniques. All participants underwent functional brain imaging to collect resting-state brain activity. The significant differences in regional homogeneity (ReHo) and amplitude of low frequency (ALFF) between the TN and control groups were calculated. After familywise error (FWE) correction, the differential brain regions in ReHo values between the two groups were mainly located in bilateral middle frontal gyrus, bilateral inferior cerebellum, right superior orbital frontal gyrus, right postcentral gyrus, left inferior temporal gyrus, left middle temporal gyrus, and left gyrus rectus. The differential brain regions in ALFF values between the two groups were mainly located in the left triangular inferior frontal gyrus, left supplementary motor area, right supramarginal gyrus, and right middle frontal gyrus. With the functional impairment of the central pain area, the active areas controlling memory and emotion also change during the progression of TN. There may be different central mechanisms in TN patients of different sexes, affected sides, and degrees of nerve damage. The exact central mechanisms remain to be elucidated., (© 2024. The Author(s).)

Spinal cord organoids are three-dimensional tissues derived from stem cells that recapitulate the primary morphological and functional characteristics of the spinal cord in vivo. As emerging bioengineering methods have led to the optimization of cell culture protocols, spinal cord organoids technology has made remarkable advancements in the past decade. Our literature search found that current spinal cord organoids do not only dynamically simulate neural tube formation but also exhibit diverse cytoarchitecture along the dorsal-ventral and rostral-caudal axes. Moreover, fused organoids that integrate motor neurons and other regionally specific organoids exhibit intricate neural circuits that allows for functional assessment. These qualities make spinal cord organoids valuable tools for disease modeling, drug screening, and tissue regeneration. By utilizing this emergent technology, researchers have made significant progress in investigating the pathogenesis and potential therapeutic targets of spinal cord diseases. However, at present, spinal cord organoid technology remains in its infancy and has not been widely applied in translational medicine. Establishment of the next generation of spinal cord organoids will depend on good manufacturing practice standards and needs to focus on diverse cell phenotypes and electrophysiological functionality evaluation.