xQTLatlas: a comprehensive resource for human cellular-resolution multi-omics genetic regulatory landscape.

Understanding how genetic variants influence molecular phenotypes in different cellular contexts is crucial for elucidating the molecular and cellular mechanisms behind complex traits, which in turn has spurred significant advances in research into molecular quantitative trait locus (xQTL) at the cellular level. With the rapid proliferation of data, there is a critical need for a comprehensive and accessible platform to integrate this information. To meet this need, we developed xQTLatlas (http://www.hitxqtl.org.cn/), a database that provides a multi-omics genetic regulatory landscape at cellular resolution. xQTLatlas compiles xQTL summary statistics from 151 cell types and 339 cell states across 55 human tissues. It organizes these data into 20 xQTL types, based on four distinct discovery strategies, and spans 13 molecular phenotypes. Each entry in xQTLatlas is meticulously annotated with comprehensive metadata, including the origin of the tissue, cell type, cell state and the QTL discovery strategies utilized. Additionally, xQTLatlas features multiscale data exploration tools and a suite of interactive visualizations, facilitating in-depth analysis of cell-level xQTL. xQTLatlas provides a valuable resource for deepening our understanding of the impact of functional variants on molecular phenotypes in different cellular environments, thereby facilitating extensive research efforts.

Traditional Chinese medicine for functional gastrointestinal disorders and inflammatory bowel disease: narrative review of the evidence and potential mechanisms involving the brain-gut axis.

Functional gastrointestinal disorders (FGIDs) and inflammatory bowel disease (IBD) are common clinical disorders characterized by recurrent diarrhea and abdominal pain. Although their pathogenesis has not been fully clarified, disruptions in intestinal motility and immune function are widely accepted as contributing factors to both conditions, and the brain-gut axis plays a key role in these processes. Traditional Chinese Medicine (TCM) employs a holistic approach to treatment, considers spleen and stomach impairments and liver abnormality the main pathogenesis of these two diseases, and offers a unique therapeutic strategy that targets these interconnected pathways. Clinical evidence shows the great potential of TCM in treating FGIDs and IBD. This study presents a systematic description of the pathological mechanisms of FGIDs and IBD in the context of the brain-gut axis, discusses clinical and preclinical studies on TCM and acupuncture for the treatment of these diseases, and summarizes TCM targets and pathways for the treatment of FGIDs and IBD, integrating ancient wisdom with contemporary biomedical insights. The alleviating effects of TCM on FGID and IBD symptoms are mainly mediated through the modulation of intestinal immunity and inflammation, sensory transmission, neuroendocrine-immune network, and microbiota and their metabolism through brain-gut axis mechanisms. TCM may be a promising treatment option in controlling FGIDs and IBD; however, further high-quality research is required. This review provides a reference for an in-depth exploration of the interventional effects and mechanisms of TCM in FGIDs and IBD, underscoring TCM's potential to recalibrate the dysregulated brain-gut axis in FGIDs and IBD.

A Recombinant Mosaic HAs Influenza Vaccine Elicits Broad-Spectrum Immune Response and Protection of Influenza a Viruses.

The annual co-circulation of two influenza A subtypes, H1N1 and H3N2, viruses in humans poses significant public health threats worldwide. However, the continuous antigenic drift and shift of influenza viruses limited the effectiveness of current seasonal influenza vaccines, necessitating the development of new vaccines against both seasonal and pandemic viruses. One potential solution to this challenge is to improve inactivated vaccines by including multiple T-cell epitopes. In this study, we designed stabilized trimeric recombinant mosaic HA proteins named HAm, which contain the most potential HA T-cell epitopes of seasonal influenza A virus. We further evaluated the antigenicity, hemagglutinin activity, and structural integrity of HAm and compared its immunogenicity and efficacy to a commercial quadrivalent inactivated influenza vaccine (QIV) in mice. Our results demonstrated that the HAm vaccine was able to induce broadly cross-reactive antibodies and T-cell responses against homologous, heterologous, and heterosubtypic influenza-naive mice. Additionally, the HAm antigens outperformed QIV vaccine antigens by eliciting protective antibodies against panels of antigenically drifted influenza vaccine strains from 2009 to 2024 and protecting against ancestral viruses' lethal challenge. These results suggest that the HAm vaccine is a promising potential candidate for future universal seasonal and pandemic influenza vaccine development.

SCancerRNA: Expression at the Single-cell Level and Interaction Resource of Non-coding RNA Biomarkers for Cancers.

Non-coding RNAs (ncRNAs) participate in multiple biological processes associated with cancers as tumor suppressors or oncogenic drivers. Due to their high stability in plasma, urine, and many other fluids, ncRNAs have the potential to serve as key biomarkers for early diagnosis and screening of cancers. During cancer progression, tumor heterogeneity plays a crucial role, and it is particularly important to understand the gene expression patterns of individual cells. With the development of single-cell RNA sequencing (scRNA-seq) technologies, uncovering gene expression in different cell types for human cancers has become feasible by profiling transcriptomes at the cellular level. However, a well-organized and comprehensive online resource that provides access to the expression of genes corresponding to ncRNA biomarkers in different cell types at the single-cell level is not available yet. Therefore, we developed the SCancerRNA database to summarize experimentally supported data on long ncRNA, microRNA, PIWI-interacting RNA, small nucleolar RNA, and circular RNA biomarkers, as well as data on their differential expression at the cellular level. Furthermore, we collected biological functions and clinical applications of biomarkers to facilitate the application of ncRNA biomarkers to cancer diagnosis, as well as the monitoring of progression and targeted therapies. SCancerRNA also allows users to explore interaction networks of different types of ncRNAs, and build computational models in the future. SCancerRNA is freely accessible at http://www.scancerrna.com/BioMarker.

Targeting RAC1 reactivates pyroptosis to reverse paclitaxel resistance in ovarian cancer by suppressing P21-activated kinase 4.

Pyroptosis may play an important role in the resistance of ovarian cancer (OC) to chemotherapy. However, the mechanism by which pyroptosis modulation can attenuate chemotherapy resistance has not been comprehensively studied in OC. Here, we demonstrated that RAS-associated C3 botulinum toxin substrate 1 (RAC1) is highly expressed in OC and is negatively correlated with patient outcomes. Through cell function tests and in vivo tumor formation tests, we found that RAC1 can promote tumor growth by mediating paclitaxel (PTX) resistance. RAC1 can mediate OC progression by inhibiting pyroptosis, as evidenced by high-throughput automated confocal imaging, the release of lactate dehydrogenase (LDH), the expression of the inflammatory cytokines IL-1ß/IL-18 and the nucleotide oligomerization domain-like receptor family pyrin domain-containing 3 (NLRP3) inflammasome. Mechanically, RNA-seq, gene set enrichment analysis (GSEA), coimmunoprecipitation (Co-IP), mass spectrometry (MS), and ubiquitination tests further confirmed that RAC1 inhibits caspase-1/gasdermin D (GSDMD)-mediated canonical pyroptosis through the P21-activated kinase 4 (PAK4)/mitogen-activated protein kinase (MAPK) pathway, thereby promoting PTX resistance in OC cells. Finally, the whole molecular pathway was verified by the results of in vivo drug combination tests, clinical specimen detection and the prognosis. In summary, our results suggest that the combination of RAC1 inhibitors with PTX can reverse PTX resistance by inducing pyroptosis through the PAK4/MAPK pathway.

CellContrast: Reconstructing spatial relationships in single-cell RNA sequencing data via deep contrastive learning.

A vast amount of single-cell RNA sequencing (SC) data have been accumulated via various studies and consortiums, but the lack of spatial information limits its analysis of complex biological activities. To bridge this gap, we introduce CellContrast, a computational method for reconstructing spatial relationships among SC cells from spatial transcriptomics (ST) reference. By adopting a contrastive learning framework and training with ST data, CellContrast projects gene expressions into a hidden space where proximate cells share similar representation values. We performed extensive benchmarking on diverse platforms, including SeqFISH, Stereo-seq, 10X Visium, and MERSCOPE, on mouse embryo and human breast cells. The results reveal that CellContrast substantially outperforms other related methods, facilitating accurate spatial reconstruction of SC. We further demonstrate CellContrast's utility by applying it to cell-type co-localization and cell-cell communication analysis with real-world SC samples, proving the recovered cell locations empower more discoveries and mitigate potential false positives.

Full-scale polymer relaxation induced by single-chain confinement enhances mechanical stability of nanocomposites.

Polymer nanocomposites with tuning functions are exciting candidates for various applications, and most current research has focused on static mechanical reinforcement. Actually, under service conditions of complex dynamic interference, stable dynamic mechanical properties with high energy dissipation become more critical. However, nanocomposites often exhibit a trade-off between static and dynamic mechanics, because of their contradictory underlying physics between chain crosslinking and chain relaxation. Here, we report a general strategy for constructing ultra-stable dynamic mechanical complex fluid nanocomposites with high energy dissipation by infusing complex fluids into the nanoconfined space. The key is to tailor full-scale polymer dynamics across an exceptionally broad timescale by single-chain confinement. These materials exhibit stable storage modulus (100 ~ 102 MPa) with high energy dissipation (loss factor > 0.4) over a broad frequency range (10-1 ~ 107 Hz)/temperature range (-35 ~ 85°C). In the loss factor > 0.4 region, their dynamic mechanical stability (rate of modulus change versus temperature (k)) is 10 times higher than that of conventional polymer nanocomposites.

Large-Area Layered Membranes with Precisely Controlled Nano-Confined Channels.

Two-dimensional (2D) nanosheets-based membranes, which have controlled 2D nano-confined channels, are highly desirable for molecular/ionic sieving and confined reactions. However, it is still difficult to develop an efficient method to prepare large-area membranes with high stability, high orientation, and accurately adjustable interlayer spacing. Here, we present a strategy to produce metal ion cross-linked membranes with precisely controlled 2D nano-confined channels and high stability in different solutions using superspreading shear-flow-induced assembly strategy. For example, membranes based on graphene oxide (GO) exhibit interlayer spacing ranging from 8.0±0.1 Što 10.3±0.2 Å, with a precision of down to 1 Å. At the same time, the value of the orientation order parameter (f) of GO membranes is up to 0.95 and GO membranes exhibit superb stability in different solutions. The strategy we present, which can be generalized to the preparation of 2D nano-confined channels based on a variety of 2D materials, will expand the application scope and provide better performances of membranes.

Recent Progress in Polyion Complex Nanoparticles with Enhanced Stability for Drug Delivery.

Polyion complex (PIC) nanoparticles, including PIC micelles and PICsomes, are typically composed of poly(ethylene glycol) block copolymers coupled with oppositely charged polyelectrolytes or therapeutic agents via electrostatic interaction. Due to a simple and rapid preparation process with high drug-loading efficiency, PIC nanoparticles are beneficial to maintaining the chemical integrity and high biological activity of the loaded drugs. However, the stability of PIC nanoparticles can be disrupted in high-ionic-strength solutions because electrostatic interaction is the DRIVING force; these disruptions can thus impair drug delivery. Herein, we summarize the advances in the use of PIC nanoparticles for delivery of charged drugs, focusing on the different chemical and physical strategies employed to enhance their stability, including enhancing the charge density, crosslinking, increasing hydrophobic interactions, forming hydrogen bonds, and the development of PIC-based gels. In particular, we describe the use of PIC nanoparticles to load peptide antibiotics targeting antibiotic-resistant and biofilm-related diseases and the use of nanoparticles that load chemotherapeutics and gaseous donors for cancer treatment. Furthermore, the application of PIC nanoparticles as magnetic resonance imaging contrast agents is summarized for the first time. Therefore, this review is of great significance for advances in the use of polymeric nanoparticles for functional drug delivery.

Development and applications of lipid hydrophilic headgroups for nucleic acid therapy.

Nucleic acid therapy is currently the most promising method for treating tumors and genetic diseases and for preventing infectious diseases. However, the biggest obstacle to this therapy is delivery of the nucleic acids to the target site, which requires overcoming problems such as capture by the immune system, the need to penetrate biofilms, and degradation of nucleic acid performance. Designing suitable delivery vectors is key to solving these problems. Lipids-which consist of a hydrophilic headgroup, a linker, and a hydrophobic tail-are crucial components for the construction of vectors. The headgroup is particularly important because it affects the drug encapsulation rate, the vector cytotoxicity, and the transfection efficiency. Herein, we focus on various headgroup structures (tertiary amines, quaternary ammonium salts, peptides, piperazines, dendrimers, and several others), and we summarize and classify important lipid-based carriers that have been developed in recent years. We also discuss applications of cationic lipids with various headgroups for delivery of nucleic acid drugs, and we analyze how headgroup structure affects transport efficiency and carrier toxicity. Finally, we briefly describe the challenges of developing novel lipid carriers, as well as their prospects.

Responses of colonization and development of periphytic biofilms to three typical tire wear particles with or without incubation-aging in migrating aqueous phases.

Understanding the behavior of tire wear particles (TWPs) and their impact on aquatic environments after aging is essential. This study explored the characteristics of TWPs generated using different methods (rolling friction, sliding friction, and cryogenic milling) and their transformation after exposure to environmental conditions mimicking runoff and sewage, focusing on their effects on river water and periphytic biofilms. Laboratory experiments indicate that at low exposure levels (0.1 mg/L), TWPs promoted biofilm growth, likely due to zinc release acting as a nutrient and the aggregation of particles serving as biofilm scaffolds. However, at higher concentrations (100 mg/L), TWPs inhibited biofilm development. This inhibition is linked to toxic byproducts like N-(1,3-dimethylbutyl)-N'-phenyl-p-phenylenediamine-quinone and environmentally persistent free radicals, which reduce biofilm biomass, alter algal diversity, and decrease the production of essential biofilm components such as proteins and polysaccharides, consistent with the inhibitory behavior of TWPs on bis-(3'-5')-cyclic diguanosine monophosphate and quorum sensing signals, including acyl-homoserine lactone and autoinducer-2. Aging processes, particularly after simulated sewage treatment, further affect ecological impacts of TWPs, reducing the benefits observed at low concentrations and intensifying the negative effects at high concentrations. Contribution of here lies in systematically revealing the impact of TWPs on the development of aquatic biofilms, emphasizing the logical relationship between their aging characteristics, environmental behavior, and ecological risks. It assesses not only the release effects of typical additives and conventional size effects but also highlights the emerging photochemical toxicity (persistent free radicals), thus providing valuable insights into the aquatic ecological risk assessment of TWPs.

Removal and assessment of cadmium contamination based on the toxic responds of a soil ciliate Colpoda sp.

Bioremediation of cadmium (Cd) pollution, a recognized low-carbon green environmental protection technology, is significantly enhanced by the discovery of Cd-tolerant microorganisms and their underlying tolerance mechanisms. This study presents Colpoda sp., a soil ciliate with widespread distribution, as a novel bioindicator and bioremediator for Cd contamination. With a 24 h-LC50 of 5.39 mg l-1 and an IC50 of 24.85 µg l-1 in Cd-contaminated water, Colpoda sp. achieves a maximum bioaccumulation factor (BAF) of 3.58 and a Cd removal rate of 32.98 ± 0.74 % within 96 h. The toxic responses of Colpoda sp. to Cd stress were assessed through cytological observation with transmission electron microscopy (TEM), oxidative stress kinase activity, and analysis of Cd-metallothionein (Cd-MTs) and the cd-mt gene via qRT-PCR. The integrated biomarker response index version 2 (IBRv2) and structural equation models (SEM) were utilized to analyze key factors and mechanisms, revealing that the up-regulation of Cd-MTs and cd-mt expression, rather than the oxidative stress system, is the primary determinant of Cd accumulation and tolerance in Colpoda sp. The ciliate's ability to maintain growth under 24.85 µg l-1 Cd stress and its capacity to absorb and accumulate Cd particles from water into cells are pivotal for bioremediation. A new mathematical formula and regression equations based on Colpoda sp.'s response parameters have been established to evaluate environmental Cd removal levels and design remediation schemes for contaminated sites. These findings provide a novel bioremediation and monitoring pathway for Cd remobilization and accumulation in soil and water, potentially revolutionizing the governance of Cd pollution.

Metformin-induced changes of the gut microbiota in patients with type 2 diabetes mellitus: results from a prospective cohort study.

BACKGROUND: The influence of the microbiota on hypoglycemic agents is becoming more apparent. The effects of metformin, a primary anti-diabetes drug, on gut microbiota are still not fully understood. RESEARCH DESIGN AND METHODS: This prospective cohort study aims to investigate the longitudinal effects of metformin on the gut microbiota of 25 treatment-naïve diabetes patients, each receiving a daily dose of 1500 mg. Microbiota compositions were analyzed at baseline, and at 1, 3, and 6 months of medication using 16S rRNA gene sequencing. RESULTS: Prior to the 3-month period of metformin treatment, significant improvements were noted in body mass index (BMI) and glycemic-related parameters, such as fasting blood glucose (FPG) and hemoglobin A1c (HbA1c), alongside homeostasis model assessment indices of insulin resistance (HOMA-IR). At the 3-month mark of medication, a significant reduction in the α-diversity of the gut microbiota was noted, while ß-diversity exhibited no marked variances throughout the treatment duration. The Firmicutes to Bacteroidetes ratio. markedly decreased. Metformin treatment consistently increased Escherichia-Shigella and decreased Romboutsia, while Pseudomonas decreased at 3 months. Fuzzy c-means clustering identified three longitudinal trajectory clusters for microbial fluctuations: (i) genera temporarily changing, (ii) genera continuing to decrease (Bacteroides), and (iii) genera continuing to increase(Lachnospiraceae ND3007 group, [Eubacterium] xylanophilum group, Romboutsia, Faecalibacterium and Ruminococcaceae UCG-014). The correlation matrix revealed associations between specific fecal taxa and metformin-related clinical parameters HbA1c, FPG, Uric Acid (UA), high-density lipoproteincholesterol (HDL-C), alanine aminotransferase (ALT), hypersensitive C-reactive protein (hs-CRP), triglyceride (TG) (P < 0.05). Metacyc database showed that metformin significantly altered 17 functional pathways. Amino acid metabolism pathways such as isoleucine biosynthesis predominated in the post-treatment group. CONCLUSIONS: Metformin's role in glucose metabolism regulation may primarily involve specific alterations in certain gut microbial species rather than an overall increase in microbial species diversity. This may suggest gut microbiota targets in future studies on metabolic abnormalities caused by metformin.

Enhancing Major Depressive Disorder Diagnosis With Dynamic-Static Fusion Graph Neural Networks.

Major Depressive Disorder (MDD) is a debilitating, complex mental condition with unclear mechanisms hindering diagnostic progress. Research links MDD to abnormal brain connectivity using functional magnetic resonance imaging (fMRI). Yet, existing fMRI-based MDD models suffer from limitations, including neglecting dynamic network traits, lacking interpretability, and struggling with small datasets. We present DSFGNN, a novel graph neural network framework addressing these issues for improved MDD diagnosis. DSFGNN employs a graph isomorphism encoder to model static and dynamic brain networks, achieving effective fusion of temporal and spatial information through a spatiotemporal attention mechanism, thereby enhancing interpretability. Furthermore, we incorporate a causal disentangling module and orthogonal regularization module to augment the model's expressiveness. We evaluate DSFGNN on the Rest-meta-MDD dataset, yielding superior results compared to the best baseline. Besides, extensive ablation studies and interpretability analysis confirm DSFGNN's effectiveness and potential for biomarker discovery.

Morphological variation in Schizothorax oconnori, Schizothorax waltoni (Teleostei: Cyprinidae: Schizothoracinae), and their natural hybrids from the middle Yarlung Zangbo River, Tibet.

The morphological variation in Schizothorax oconnori, Schizothorax waltoni, and their natural hybrids was examined using conventional and image-based analysis approaches. In total, 38 specimens of S. oconnori, 35 of S. waltoni, and 37 natural hybrids were collected from the Shigatse to the Lhasa section of the Yarlung Zangbo River during June and July 2021. A total of 21 morphometric, 4 meristic, and 27 truss variables were employed for the classification of S. oconnori, S. waltoni, and natural hybrids. Principal component analysis (PCA) and factor analysis (FA), as well as discriminant function analysis (DFA) and cluster analysis (CA), were conducted to identify differences based on traditional and truss measurements. Four principal components explained 75.92% of the variation among the morphometric characters, while five principal components accounted for 79.69% of the variation among the truss distances. FA results showed that factor 1 was associated with head shape, and factor 2 was associated with fins based on morphometric characters. Among the truss characters, factor 1 was related to head shape, and factor 2 was related to chest shape. In DFA, morphometric measurements achieved higher accuracy (100%) compared to truss distances (94.55%). The head morphology of hybrids exhibited intermediate traits between S. oconnori and S. waltoni. Both morphometry-based and truss-based clustering indicated that the morphology of natural hybrids leaned toward S. oconnori. In conclusion, the combination of morphometric and truss analysis is beneficial for classifying S. oconnori, S. waltoni, and their natural hybrids. The presence of natural hybrids could be considered an evolutionary response to the differentiation of nutritional and spatial niches in the middle Yarlung Zangbo River.

Open access EEG dataset of repeated measurements from a single subject for microstate analysis.

Electroencephalography (EEG) microstate analysis is a neuroimaging analytical method that has received considerable attention in recent years and is widely used for analysing EEG signals. EEG is easily influenced by internal and external factors, which can affect the repeatability and stability of EEG microstate analysis. However, there have been few reports and publicly available datasets on the repeatability of EEG microstate analysis. In the current study, a 39-year-old healthy male underwent a total of 60 simultaneous electroencephalography and electrocardiogram measurements over a period of three months. After the EEG recording was completed, magnetic resonance imaging (MRI) was also conducted. To date, this EEG dataset has the highest number of repeated measurements for one individual. The dataset can be used to assess the stability and repeatability of EEG microstates and other analytical methods, to decode resting EEG states among subjects with open eyes, and to explore the stability and repeatability of cortical spatiotemporal dynamics through source analysis with individual MRI.

PUTransGCN: identification of piRNA-disease associations based on attention encoding graph convolutional network and positive unlabelled learning.

Piwi-interacting RNAs (piRNAs) play a crucial role in various biological processes and are implicated in disease. Consequently, there is an escalating demand for computational tools to predict piRNA-disease interactions. Although there have been computational methods proposed for the detection of piRNA-disease associations, the problem of imbalanced and sparse dataset has brought great challenges to capture the complex relationships between piRNAs and diseases. In response to this necessity, we have developed a novel computational architecture, denoted as PUTransGCN, which uses heterogeneous graph convolutional networks to uncover potential piRNA-disease associations. Additionally, the attention mechanism was used to adjust the weight parameters of aggregation heterogeneous node features automatically. For tackling the imbalanced dataset problem, the combined positive unlabelled learning (PUL) method comprising PU bagging, two-step and spy technique was applied to select reliable negative associations. The features of piRNAs and diseases were derived from three distinct biological sources by PUTransGCN, including information on piRNA sequences, semantic terms related to diseases and the existing network of piRNA-disease associations. In the experiment, PUTransGCN performs in 5-fold cross-validation with an AUC of 0.93 and 0.95 on two datasets, respectively, which outperforms the other six state-of-the-art models. We compared three different PUL methods, and the results of the ablation experiment indicate that the combined PUL method yields the best results. The PUTransGCN could serve as a valuable piRNA-disease prediction tool for upcoming studies in the biomedical field. The code for PUTransGCN is available at https://github.com/chenqiuhao/PUTransGCN.

Long-read sequencing unveils novel somatic variants and methylation patterns in the genetic information system of early lung cancer.

Lung cancer poses a global health challenge, necessitating advanced diagnostics for improved outcomes. Intensive efforts are ongoing to pinpoint early detection biomarkers, such as genomic variations and DNA methylation, to elevate diagnostic precision. We conducted long-read sequencing on cancerous and adjacent non-cancerous tissues from a patient with lung adenocarcinoma. We identified somatic structural variations (SVs) specific to lung cancer by integrating data from various SV calling methods and differentially methylated regions (DMRs) that were distinct between these two tissue samples, revealing a unique methylation pattern associated with lung cancer. This study discovered over 40,000 somatic SVs and over 180,000 DMRs linked to lung cancer. We identified approximately 700 genes of significant relevance through comprehensive analysis, including genes intricately associated with many lung cancers, such as NOTCH1, SMOC2, CSMD2, and others. Furthermore, we observed that somatic SVs and DMRs were substantially enriched in several pathways, such as axon guidance signaling pathways, which suggests a comprehensive multi-omics impact on lung cancer progression across various biological investigation levels. These datasets can potentially serve as biomarkers for early lung cancer detection and may hold significant value in clinical diagnosis and treatment applications.

Evaluation and analysis of surgical treatment for single-level or multi-level lumbar degenerative disease based on radiography.

Background: Radiography has a low level of radiation exposure while providing valuable information. Due to its cost effectiveness and widespread availability, the preoperative radiographic imaging examination is a valuable approach for assessing patients with spinal disease. This study aimed to examine the influence of preoperative X-ray evaluation on the surgical treatment of patients with single- or multi-level lumbar degenerative disease (LDD). Methods: A retrospective cohort analysis was conducted of 172 patients diagnosed with LDD who underwent transforaminal lumbar interbody fusion (TLIF) or posterior lumbar interbody fusion (PLIF) surgery between December 2021 and February 2023 at the Shanghai Changzheng Hospital. Various parameters were measured on preoperative radiographs, including the iliac crest height, median iliac angle (MIA), lumbar lordosis (LL), intervertebral facet joint degeneration, lumbosacral angle (LSA), intervertebral foramen height (IFH), and surgical segment. The surgical treatment was evaluated based on the operative time, intraoperative blood loss, and postoperative complications. A correlation analysis and independent sample t-tests were used to assess the relationship between preoperative radiographic variables and surgical treatments. Further, a multivariate linear regression analysis was employed to identify the risk factors affecting the clinical outcomes. Results: The correlation analysis and t-test results showed that the MIA, height of the iliac crest, intervertebral facet joint degeneration, and surgical segment were significantly correlated with the surgical treatments (P<0.05). Specifically, the height of the iliac crest, intervertebral facet joint degeneration, and surgical segment were positively correlated with the surgical treatments. Conversely, the MIA was negatively correlated with the surgical treatments. However, no significant differences were observed between the IFH, LSA, and LL in relation to posterior lumbar surgery (P>0.05). The multiple linear regression analysis showed that the height of the iliac crest, MIA, intervertebral facet joint degeneration, and surgical segment were independent factors affecting the surgical treatments of patients with single- or multi-level LDD. These findings highlight the importance of considering these factors when planning and performing lumbar surgery. Conclusions: The measurements taken from radiographs, including the height of the iliac crest, MIA, intervertebral facet joint degeneration, and surgical segment, demonstrate potential influences on the treatment of single- and multi-level lumbar spine surgery. These variables can be captured in plain film imaging and can provide valuable insights into the surgical procedure and offer guidance for the operation. By analyzing these radiographic measurements, surgeons can gain a better understanding of a patient's condition and tailor the surgical approach accordingly, thus optimizing the outcomes of the surgery.