Mo-doped carbon-dots nanozyme with peroxide-like activity for sensitive and selective smartphone-assisted colorimetric S2- ion detection and antibacterial application.

Sulfur ion (S2-) plays a significant and considerable role in many living organisms and ecosystems, while its abnormal content can pose a serious hazard to human health and ecological environment. Hence, it is extremely meaningful to construct a highly sensitive and selective analytical platform for S2- detection in complex microenvironment, particularly in biological systems. In this study, phosphomolybdic acid and L-Arg were utilized to prepare a new molybdenum doped carbon-dots nanozyme (Mo-CDs) with great peroxidase-like activity by one-step hydrothermal approach. In the presence of H2O2, Mo-CDs converted 3,3',5,5'-tetramethyl benzidine (TMB) into blue oxTMB, but S2- strongly reduced the blue solution to colorless and then brown, which established significant selectivity toward S2-. Mo-CDs illustrated a wide linear range (2.5 µM-900 µM) and low detection limit (LOD = 76 nM) by ultraviolet and smartphone-assisted visualized colorimetric analysis. Especially, the smartphone-assisted analysis platform successfully realized quick, portable, sensitive and visible identification of S2- with high recovery (95.7-106.7 %) and excellent specificity in water samples. More importantly, Mo-CDs was developed to antibacterial applications based on good peroxidase-like activity. This research not only constructed a new and efficient carbon-dots nanozyme and a low-cost, portable, visual analysis platform for real-time detection of S2-, but also proposed a novel design strategy and methodology for exploiting multifunctional nanozyme detection tool with great practical application.

Mapping the research of mitochondria and Parkinson's disease: a bibliometric analysis.

Background: Parkinson's disease (PD) is a chronic, progressive neurodegenerative disorder primarily affecting the elderly. Relevant studies suggest a significant connection between the mitochondria and PD. Publications exploring this connection have steadily increased in recent years. This study employs a bibliometric approach to comprehensively analyze the current status and future directions of the research on mitochondria and PD. Method: We retrieved data from the Web of Science database and used CiteSpace, VOSviewer, and "Bibliometrix" software to visually analyze various aspects of the research field. These aspects included the number of published papers, contributing countries and institutions, authors, publishing journals, cited references, and keywords. Results: Our analysis identified a total of 3,291 publications involving 14,670 authors from 2,836 organizations across 78 countries. The publication volume exhibited a continuous upward trend from 1999 to 2023. The United States emerged as the leading force in this research area, contributing the highest number of high-quality publications. Notably, the United States collaborated extensively with Germany and the United Kingdom. The University of Pittsburgh stood out as the most prolific institution. Harvard University had the highest academic influence and closely cooperated with the University of Pittsburgh, Juntendo University, and McGill University. Dr. Hattori Nobutaka was identified as the most prolific author, while Dr. Youle, Richard J emerged as the most influential author based on the highest average citation frequency. The Journal of Neurochemistry was the most published journal. The most co-cited paper was titled "Hereditary early-onset Parkinson's disease caused by mutations in PINK1." The major keywords included oxidative stress, alpha-synuclein, pink1, mitophagy, and mitochondrial dysfunction. Mitofusin 2, ubiquitin, and mitochondrial quality control have been identified as new research hotspots in recent years. Conclusion: Mitochondria-PD research is experiencing a steady increase in activity, fueled by increasing close collaboration between countries and different institutions. However, there is a need to further strengthen collaboration and communication between developed and developing nations. Current research has focused on the specific mechanisms of mitochondrial dysfunction and their relationship with PD. Mitofusin 2, ubiquitin, and mitochondrial quality control are positioned to be the hotspots and future research directions.

Prognostic Value of Fibrosis-4 in Acute Ischemic Stroke Patients Undergoing Intravenous Thrombolysis.

Purpose: Although recombinant tissue plasminogen activator (rt-PA) treatment is efficient in patients with acute ischemic stroke (AIS), a significant percentage of patients who received rt-PA intravenous thrombolysis (IVT) do not achieve a good prognosis. Therefore, the factors that affect the poor prognosis of patients with IVT are needed. The Fibrosis-4 (FIB-4) index has been used as a liver fibrosis biomarker. We aimed to investigate the relationship between the FIB-4 index and functional outcomes in patients with AIS receiving IVT. Patients and Methods: This study prospectively included consecutive patients with AIS receiving IVT between April 2015 and May 2022. We collected clinical and laboratory data and calculated the FIB-4 index. Clinical outcome was poor functional outcome (mRS ≥3) at 3 months after IVT. Multivariate logistic regression analysis was used to analyze the association between FIB-4 and outcome. We explored the interactive effect of FIB-4 and dyslipidemia on poor outcomes, and subgroup analysis was performed. Furthermore, an individualized prediction model based on the FIB-4 for functional outcome was established in the dyslipidemia group. Results: A total of 1135 patients were included, and 41.50% had poor 3-month outcomes. After adjusted by other variants that P value <0.05 in univariable analysis, FIB-4 was independently associated with poor outcomes (OR=1.420; 95% CI: 1.113-1.812; P=0.004). There was a significant interaction between FIB-4 and dyslipidemia on poor outcome (P=0.036), and the independent association between FIB-4 and poor outcome was maintained in the dyslipidemia subgroup (OR=1.646; 95% CI: 1.228-2.206; P=0.001). Furthermore, in the dyslipidemia group, the FIB-4-based prediction model had good predictive value (the AUC of the training and validation sets were 0.767 and 0.708, respectively), good calibration (P-values for the Hosmer-Lemeshow test >0.05), and clinical usefulness. Conclusion: FIB-4 is an independent risk factor for poor outcomes in IVT patients with dyslipidemia, which can be used as a simple predictor of their prognosis.

Adaptive Privacy-Preserving Coded Computing with Hierarchical Task Partitioning.

Coded computing is recognized as a promising solution to address the privacy leakage problem and the straggling effect in distributed computing. This technique leverages coding theory to recover computation tasks using results from a subset of workers. In this paper, we propose the adaptive privacy-preserving coded computing (APCC) strategy, designed to be applicable to various types of computation tasks, including polynomial and non-polynomial functions, and to adaptively provide accurate or approximated results. We prove the optimality of APCC in terms of encoding rate, defined as the ratio between the computation loads of tasks before and after encoding, based on the optimal recovery threshold of Lagrange Coded Computing. We demonstrate that APCC guarantees information-theoretical data privacy preservation. Mitigation of the straggling effect in APCC is achieved through hierarchical task partitioning and task cancellation, which further reduces computation delays by enabling straggling workers to return partial results of assigned tasks, compared to conventional coded computing strategies. The hierarchical task partitioning problems are formulated as mixed-integer nonlinear programming (MINLP) problems with the objective of minimizing task completion delay. We propose a low-complexity maximum value descent (MVD) algorithm to optimally solve these problems. The simulation results show that APCC can reduce the task completion delay by a range of 20.3% to 47.5% when compared to other state-of-the-art benchmarks.

Change trend and attribution analysis of leaf area index in the East African Plateau from 1982 to 2020.

The ecosystems on the East African Plateau are crucial for maintaining the biodiversity, water resource balance, and ecological equilibrium of the African continent. However, the spatiotemporal variations of vegetation and the driving factors remain unclear. We analyzed leaf area index (LAI) change trends in the East African Plateau based on the GIMMS LAI4g dataset and further conducted attribution analysis combining temperature and precipitation data, as well as 10 Dynamic Global Vegetation Models (DGVMs) in TRNEDY v9. The results showed that LAI of the East African Plateau had a modest change trend from 1982 to 1999 (2.5×10-3 m2·m-2·a-1), but significantly increased from 2000 to 2020 (5.2×10-3 m2·m-2·a-1), which was 2.1 times faster than that during 1982-1999. Temperature and precipitation had weak correlations with LAI from 1982 to 1999, but showed significant correlations from 2000 to 2020. The DGVMs demonstrated consistent attribution results, with temperature and precipitation contributing significantly more to the LAI variations from 2000 to 2020 compared to the period from 1982 to 1999. The results highlighted the key role of climate change in driving vegetation greening on the East African Plateau during 2000-2020, which could provide important evidence for ecological conservation and sustainable development strategies in the region.

Exploring the correlation between DNA methylation and biological age using an interpretable machine learning framework.

DNA methylation plays a significant role in regulating transcription and exhibits a systematic change with age. These changes can be used to predict an individual's age. First, to identify methylation sites associated with biological age; second, to construct a biological age prediction model and preliminarily explore the biological significance of methylation-associated genes using machine learning. A biological age prediction model was constructed using human methylation data through data preprocessing, feature selection procedures, statistical analysis, and machine learning techniques. Subsequently, 15 methylation data sets were subjected to in-depth analysis using SHAP, GO enrichment, and KEGG analysis. XGBoost, LightGBM, and CatBoost identified 15 groups of methylation sites associated with biological age. The cg23995914 locus was identified as the most significant contributor to predicting biological age by calculating SHAP values. Furthermore, GO enrichment and KEGG analyses were employed to initially explore the methylated loci's biological significance.

Imatinib mesylate reduces c-MYC expression in double-hit lymphoma cells by suppressing inducible cytidine deaminase.

BACKGROUND: Double-hit lymphoma (DHL) with c-MYC gene translocation is highly aggressive and has a poor prognosis. In DHL cells, activation-induced cytidine deaminase (AID) promotes antibody class switch recombination (CSR), ultimately leading to c-MYC gene translocation caused by Myc/IgH DNA double-strand breaks. However, currently there is still no method to suppress the expression of AID. METHODS: In this study, we compared the clinical significance of AID expression in DHL, Additionally, two human double-hit lymphoma cell lines were used to analyze the effect of imatinib mesylate on c-MYC in vitro, and the therapeutic effect was also evaluated in xenograft mouse models. RESULTS: Imatinib mesylate downregulated the AID and c-MYC proteins in patients with chronic myelogenous leukemia associated with DHL. In addition, imatinib mesylate reduced AID and c-MYC expression in SU-DHL-4 and OCI-Ly18 DHL cells. Imatinib mesylate exerted significant inhibitory effects on the proliferation and metastasis of SU-DHL-4 and OCI-Ly18 cells. Finally, imatinib mesylate reduced not only tumor burden in DHL mouse models, but also AID and c-MYC expression in vivo. CONCLUSION: These findings reveal that imatinib mesylate effectively reduces the carcinogenic function of c-MYC in DHL, providing novel strategies for developing therapies targeting c-MYC-driven DHL.

Role of naringin in the treatment of atherosclerosis.

Atherosclerosis (AS) is a major pathological basis of coronary heart disease. However, the currently available medications are unable to effectively reduce the incidence of cardiovascular events in the majority of patients with AS. Therefore, naringin has been attracting considerable attention owing to its anti-AS effects. Naringin can inhibit the growth, proliferation, invasion, and migration of vascular smooth muscle cells, ameliorate endothelial cell inflammation and apoptosis, lower blood pressure, halt the cell cycle at the G1 phase, and impede growth via its antioxidant and free radical scavenging effects. These activities suggest the potential anti-AS effects of naringin. In this review article, we comprehensively summarized the latest findings on the anti-AS effects of naringin and their underlying mechanisms, providing a crucial reference for future research on the anti-AS potential of this agent.

Realizing Excellent Infrared Nonlinear Optical Performance in Eu-Based Chalcogenides via Rational Cross Substitution Strategy.

In recent years, rare-earth-based chalcogenides have gained attention promising materials in the field of infrared nonlinear optical (IR-NLO) applications owing to their exceptional physicochemical properties. However, they frequently encounter challenges such as adverse two-photon absorption and low laser-induced damage thresholds (LIDTs) caused by narrow optical band gaps (Eg), which limit their practical utility. In this study, we started with the centrosymmetric (CS) parent compound EuGa2S4 to develop two new noncentrosymmetric (NCS) Eu-based chalcogenides, namely, EuZnSiS4 and EuCdSiS4, employing a rational cross-substitution strategy. Despite having identical stoichiometry, both compounds crystallize in distinct NCS orthorhombic space groups [Fdd2 (no. 43) vs Ama2 (no. 40)], as confirmed by single-crystal structure analysis. Their crystal structures feature highly distorted tetrahedral motifs interconnected via corner-sharing, forming unique two-dimensional layers that host Eu2+ cations. Furthermore, both compounds exhibit robust phase-matching second-harmonic generation (SHG) intensities of 1.5 × AgGaS2 for EuZnSiS4 and 2.8 × AgGaS2 for EuCdSiS4 under 2050 nm excitation. They also demonstrate high LIDTs (approximately 14-17 × AgGaS2), wide Eg (>2.5 eV), and transparency windows extending up to 18.2 µm. Particularly noteworthy, EuCdSiS4 stands out as a pioneering example in the Eu-based IR-NLO system for successfully combining a broad Eg (>2.56 eV, equivalent to that of AgGaS2) with a significant SHG effect (>1.0 × AgGaS2) simultaneously. Structural analyses and theoretical insights underscore that the reasonable combination of asymmetric functional units plays a pivotal role in driving the CS-to-NCS structural transformation and enhancing the NLO and linear optical properties of these Eu-based chalcogenides. This study presents a promising chemical pathway for advancing rare-earth-based functional materials and suggests exciting opportunities for their future applications in IR-NLO technologies.

Lipocalin-2 aggravates blood-brain barrier dysfunction after intravenous thrombolysis by promoting endothelial cell ferroptosis via regulating the HMGB1/Nrf2/HO-1 pathway.

BACKGROUND: Disruption of the blood-brain barrier (BBB) is a major contributor to hemorrhagic transformation (HT) in patients with acute ischemic stroke (AIS) following intravenous thrombolysis (IVT). However, the clinical therapies aimed at BBB protection after IVT remain limited. METHODS: One hundred patients with AIS who underwent IVT were enrolled (42 with HT and 58 without HT 24 h after IVT). Based on the cytokine chip, the serum levels of several AIS-related proteins, including LCN2, ferritin, matrix metalloproteinase-3, vascular endothelial-derived growth factor, and X-linked inhibitor of apoptosis, were detected upon admission, and their associations with HT were analyzed. After finding that LCN2 was related to HT in patients with IVT, we clarified whether the modulation of LCN2 influenced BBB dysfunction and HT after thrombolysis and investigated the potential mechanism. RESULTS: In patients with AIS following IVT, logistic regression analysis showed that baseline serum LCN2 (p = 0.023) and ferritin (p = 0.046) levels were independently associated with HT. A positive correlation between serum LCN2 and ferritin levels was identified in patients with HT. In experimental studies, recombinant LCN2 (rLCN2) significantly aggravated BBB dysfunction and HT in the thromboembolic stroke rats after thrombolysis, whereas LCN2 inhibition by ZINC006440089 exerted opposite effects. Further mechanistic studies showed that, LCN2 promoted endothelial cell ferroptosis, accompanied by the induction of high mobility group box 1 (HMGB1) and the inhibition of nuclear translocation of nuclear factor E2-related factor 2 (Nrf2) and heme oxygenase-1 (HO-1) proteins. Ferroptosis inhibitor ferrostatin-1 (fer-1) significantly restricted the LCN2-mediated BBB disruption. Transfection of LCN2 and HMGB1 siRNA inhibited the endothelial cell ferroptosis, and this effects was reversed by Nrf2 siRNA. CONCLUSION: LCN2 aggravated BBB disruption after thrombolysis by promoting endothelial cell ferroptosis via regulating the HMGB1/Nrf2/HO-1 pathway, this may provide a promising therapeutic target for the prevention of HT after IVT.

Design, synthesis and antimycobacterial activity of novel benzothiazinones with improved water solubility.

Nitrobenzothiazinones (BTZs) represent a novel type of antitubercular agents targeting DprE1. Two clinical candidates BTZ043 and PBTZ169, as well as many other BTZs showed potent anti-TB activity, but they are all highly lipophilic and their poor aqueous solubility is still a serious issue need to be addressed. Here, we designed and synthesized a series of new BTZ derivatives, wherein a hydrophilic COOH or NH2 group is directly attached to the oxime moiety of TZY-5-84 discovered in our lab, through various linkers. Two compounds 1a and 3 were first reported to possess excellent activity against MTB H37Rv and MDR-MTB strains (MIC: <0.029-0.095 µM), low toxicity and acceptable oral PK profiles, as well as significantly improved water solubility (1200 and > 2000 µg/mL, respectively), suggesting they may serve as promising hydrophilic BTZs for further antitubercular drug discovery.

Screening Strategy Identifies an Overlooked Deep-Ultraviolet Transparent Nonlinear Optical Crystal.

In the deep-ultraviolet (DUV) region, nonlinear optical (NLO) crystals must meet stringent requirements, including a large optical band gap and sufficient second harmonic generation (SHG) response. Typically, these criteria are fulfilled by borates, carbonates and nitrates containing π-conjugated groups. In contrast, sulfates and phosphates, with polarizabilities significantly smaller than those of π-conjugated groups, struggle to achieve similar performance. Here, we present the discovery of Mg2PO4Cl, a magnesium-based phosphate, identified from over 10,000 phosphates based on a polar-axial-symmetry screening strategy, which exhibits the highest SHG response (5.2×KH2PO4 (KDP)) with phase-matching ability among non-π-conjugated DUV transparent NLO crystals. This compound belongs to the Pna21 space group, with [PO4] units consistently aligned along the 21 screw axis and glide planes throughout its crystal structure. Theoretical calculations attribute its remarkable SHG effect to the orderly arrangement of heteroanionic [MgO5Cl] and [MgO4Cl2] polyhedra alongside isolated [PO4] tetrahedra, supported by Berry phase analysis. Furthermore, a crystallographic structure analysis of phosphates and sulfates with significant SHG effects validates the effectiveness of our screening strategy. These findings offer valuable insights into the origins of NLO effects in non-π-conjugated compounds from both a material design and structural chemistry perspective, inspiring future efforts to revitalize DUV phosphates.

Preparation of multi-modified/carbonized/gelatinized starch and its de-risking effect on Cd(II) and hymexazol in wastewater.

In this study, starch (S) was gelatinized and carbonized to prepare carbonized/gelatinized S (CGS) as the research material. Then, peat extract (Pe) and surfactants with different ratios were single- and multi-modified on CGS, respectively, to prepare Pe-modified CGS (Pe-CGS) and multi-modified CGS, respectively. The microscopic morphology of multi-modified CGS was studied using various testing methods. The de-risking effect on Cd(II) and hymexazol in wastewater was investigated, and the effects of temperature, pH, and ionic strength were compared. The spheroidal structure of S was destroyed after carbonization, and Pe and surfactants were modified on the surface and changed the surface properties of CGS. The adsorption processes of Cd(II) and hymexazol were suitable to be described by the Langmuir and Freundlich models, respectively. The maximum adsorption capacities (qm) of Cd(II) and adsorption capacity parameter (k) of hymexazol on different modified CGSs presented the peak value at BS/Pe-CGS. With the increase in the modification ratio of Pe, BS, and SDS, qm and k increased, which showed a high value at 100 % modification. Increases in temperature and pH were beneficial to Cd(II) adsorption but were not conducive to hymexazol adsorption. The adsorption amount decreased for Cd(II) and increased first and then reduced for hymexazol with the rise in ionic strength. The adsorption process exhibited spontaneity, endothermic behavior for Cd(II), exothermic behavior for hymexazol, and an entropy-increasing reaction. The adsorption amount of Cd(II) and hymexazol by multi-modified CGS maintained approximately 81 % of the original sample after three rounds of regeneration.

The tripartite motif-containing 24 is a multifunctional player in human cancer.

Tripartite motif-containing 24 (TRIM24), also known as transcriptional intermediary factor 1α (TIF1α), is the founding member of TIF1 family. Recent evidence indicates that aberrant expression of TRIM24, functions as an oncogene, is associated with poor prognosis across various cancer types. TRIM24 exhibits a multifaceted structure comprising an N-terminal TRIM region with a RING domain, B-box type 1 and type 2 domains, and a coiled-coil region, as well as a C-terminal plant-homeodomain (PHD)-bromodomain. The bromodomain serves as a 'reader' of epigenetic histone marks, regulating chromatin structure and gene expression by linking associated proteins to acetylated nucleosomal targets, thereby controlling transcription of genes. Notably, bromodomains have emerged as compelling targets for cancer therapeutic development. In addition, TRIM24 plays specialized roles as a signal transduction molecule, orchestrating various cellular signaling cascades in cancer cells. Herein, we review the recent advancements in understanding the functions of TRIM24, and demonstrate the research progress in utilizing TRIM24 as a target for cancer therapy.

Association of non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio (NHHR) and gallstones among US adults aged ≤ 50 years: a cross-sectional study from NHANES 2017-2020.

BACKGROUND: The chronic digestive condition gallstones is quite common around the world, the development of which is closely related to oxidative stress, inflammatory response and abnormalities of lipid metabolism. In the last few years, as a novel biomarker of lipid metabolism, the non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio (NHHR) has garnered significant interest. However, its relationship with gallstones has not been studied yet. METHODS: 3,772 people, all under 50, were included in this study, and their full data came from the National Health and Nutrition Examination Survey (NHANES) database for the years 2017-2020. Information on gallstones was obtained through self-reported questionnaires. Smoothed curve fitting multifactorial logistic regression was utilized to evaluate the connection of NHHR with gallstone formation incidence. Subsequently, subgroup analysis and interaction tests were applied. Finally, to create a prediction model, logistic regression and feature screening by last absolute shrinkage and selection operator (LASSO) were used. The resulting model was displayed using a nomogram. RESULTS: In multivariate logistic regression that accounted for all factors, there was a 77% increase in the likelihood of gallstones for every unit rise in lnNHHR (OR 1.77 [CI 1.11-2.83]). Following NHHR stratification, the Q4 NHHR level was substantially more linked to the risk of gallstones than the Q1 level (OR 1.86 [CI 1.04-3.32]). This correlation was stronger in women, people under 35, smokers, abstainers from alcohol, non-Hispanic White people, those with excessively high cholesterol, people with COPD, and people without diabetes. After feature screening, a predictive model and visualized nomogram for gallstones were constructed with an AUC of 0.785 (CI 0.745-0.819), which was assessed by DCA to be clinically important. CONCLUSION: In the group of people ≤ 50 years of age, elevated NHHR levels were substantially linked to a higher incidence of gallstones. This correlation was stronger in several specific groups such as females, under 35 years of age, smokers, and so on. Predictive models constructed using the NHHR have potential clinical value in assessing gallstone formation.

Genome-Wide Analyses of MADS-Box Genes Reveal Their Involvement in Seed Development and Oil Accumulation of Tea-Oil Tree (Camellia oleifera).

The seeds of Camellia oleifera produce high amount of oil, which can be broadly used in the fields of food, industry, and medicine. However, the molecular regulation mechanisms of seed development and oil accumulation in C. oleifera are unclear. In this study, evolutionary and expression analyses of the MADS-box gene family were performed across the C. oleifera genome for the first time. A total of 86 MADS-box genes (ColMADS) were identified, including 60 M-type and 26 MIKC members. More gene duplication events occurred in M-type subfamily (6) than that in MIKC subfamily (2), and SEP-like genes were lost from the MIKCC clade. Furthermore, 8, 15, and 17 differentially expressed ColMADS genes (DEGs) were detected between three developmental stages of seed (S1/S2, S2/S3, and S1/S3), respectively. Among these DEGs, the STK-like ColMADS12 and TT16-like ColMADS17 were highly expressed during the seed formation (S1 and S2), agreeing with their predicted functions to positively regulate the seed organogenesis and oil accumulation. While ColMADS57 and ColMADS07 showed increasing expression level with the seed maturation (S2 and S3), conforming to their potential roles in promoting the seed ripening. In all, these results revealed a critical role of MADS-box genes in the C. oleifera seed development and oil accumulation, which will contribute to the future molecular breeding of C. oleifera.

Automatic measurement of anatomical parameters of the lumbar vertebral body and the intervertebral disc on radiographs by deep learning.

Background: Lumbar spine disorders are one of the common causes of low back pain (LBP). Objective and reliable measurement of anatomical parameters of the lumbar spine is essential in the clinical diagnosis and evaluation of lumbar disorders. However, manual measurements are time-consuming and laborious, with poor consistency and repeatability. Here, we aim to develop and evaluate an automatic measurement model for measuring the anatomical parameters of the vertebral body and intervertebral disc based on lateral lumbar radiographs and deep learning (DL). Methods: A model based on DL was developed with a dataset consisting of 1,318 lateral lumbar radiographs for the prediction of anatomical parameters, including vertebral body heights (VBH), intervertebral disc heights (IDH), and intervertebral disc angles (IDA). The mean of the values obtained by 3 radiologists was used as a reference standard. Statistical analysis was performed in terms of standard deviation (SD), mean absolute error (MAE), Percentage of correct keypoints (PCK), intraclass correlation coefficient (ICC), regression analysis, and Bland-Altman plot to evaluate the performance of the model compared with the reference standard. Results: The percentage of intra-observer landmark distance within the 3 mm threshold was 96%. The percentage of inter-observer landmark distance within the 3 mm threshold was 94% (R1 and R2), 92% (R1 and R3), and 93% (R2 and R3), respectively. The PCK of the model within the 3 mm distance threshold was 94-99%. The model-predicted values were 30.22±3.01 mm, 10.40±3.91 mm, and 10.63°±4.74° for VBH, IDH, and IDA, respectively. There were good correlation and consistency in anatomical parameters of the lumbar vertebral body and disc between the model and the reference standard in most cases (R2=0.89-0.95, ICC =0.93-0.98, MAE =0.61-1.15, and SD =0.89-1.64). Conclusions: The newly proposed model based on a DL algorithm can accurately measure various anatomical parameters on lateral lumbar radiographs. This could provide an accurate and efficient measurement tool for the quantitative evaluation of spinal disorders.

Deep learning-based quantitative morphological study of anteroposterior digital radiographs of the lumbar spine.

Background: Morphological parameters of the lumbar spine are valuable in assessing lumbar spine diseases. However, manual measurement of lumbar morphological parameters is time-consuming. Deep learning has automatic quantitative and qualitative analysis capabilities. To develop a deep learning-based model for the automatic quantitative measurement of morphological parameters from anteroposterior digital radiographs of the lumbar spine and to evaluate its performance. Methods: This study used 1,368 anteroposterior digital radiographs of the lumbar spine to train a deep learning model to measure the quantitative morphological indicators, including L1 to L5 vertebral body height (VBH) and L1-L2 to L4-L5 intervertebral disc height (IDH). The means of the manual measurements by three radiologists were used as the reference standard. The parameters predicted by the model were analyzed against the manual measurements using paired t-tests. Percentage of correct key points (PCK), intra-class correlation coefficient (ICC), Pearson correlation coefficient (r), mean absolute error (MAE), root mean square error (RMSE), and Bland-Altman plots were performed to assess the performance of the model. Results: Within the 3-mm distance threshold, the model had a PCK range of 99.77-99.46% for the L1 to L4 vertebrae and 77.37% for the L5 vertebrae. Except for VBH-L5 and IDH_L3-L4, IDH_L4-L5 (P<0.05), the estimated values of the model in the remaining parameters were not statistically significant compared with the reference standard (P>0.05). Except for VBH-L5 and IDH_L4-L5, the model showed good correlation and consistency with the reference standard (ICC =0.84-0.96, r=0.85-0.97, MAE =0.5-0.66, RMSE =0.66-0.95). The model outperformed other models (EfficientDet + Unet, EfficientDet + DarkPose, HRNet, and Unet) in predicting landmarks within a distance threshold of 1.5 to 5 mm. Conclusions: The model developed in this study can automatically measure the morphological parameters of the L1 to L4 vertebrae from anteroposterior digital radiographs of the lumbar spine. Its performance is close to the level of radiologists.

Cellular senescence: A novel therapeutic target for central nervous system diseases.

The underlying mechanisms of diseases affecting the central nervous system (CNS) remain unclear, limiting the development of effective therapeutic strategies. Remarkably, cellular senescence, a biological phenomenon observed in cultured fibroblasts in vitro, is a crucial intrinsic mechanism that influences homeostasis of the brain microenvironment and contributes to the onset and progression of CNS diseases. Cellular senescence has been observed in disease models established in vitro and in vivo and in bodily fluids or tissue components from patients with CNS diseases. These findings highlight cellular senescence as a promising target for preventing and treating CNS diseases. Consequently, emerging novel therapies targeting senescent cells have exhibited promising therapeutic effects in preclinical and clinical studies on aging-related diseases. These innovative therapies can potentially delay brain cell loss and functional changes, improve the prognosis of CNS diseases, and provide alternative treatments for patients. In this study, we examined the relevant advancements in this field, particularly focusing on the targeting of senescent cells in the brain for the treatment of chronic neurodegenerative diseases (e.g., Alzheimer's disease, Parkinson's disease, and multiple sclerosis) and acute neurotraumatic insults (e.g., ischemic stroke, spinal cord injury, and traumatic brain injury).