Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

OBJECTIVE To evaluate the efficacy， safety and cost-effectiveness of serplulimab as a first-line treatment of small- cell lung cancer （SCLC）， and provide an evidence-based basis for drug selection in hospitals. METHODS Rapid health technology assessment was adopted； PubMed， Cochrane Library， Embase， CNKI， Wanfang， VIP and official websites of domestic and international health technology assessment agencies were systematically searched from the inception to Oct. 2024. Two reviewers independently screened the literature， assessed the quality of included studies and carried out the qualitative analysis according to the inclusion and exclusion criteria. RESULTS A total of 13 systematic reviews/meta-analyses and 9 economic studies were included， and the literature quality was generally good. In terms of effectiveness， compared with chemotherapy alone， serplulimab combined with chemotherapy significantly improved progression-free survival， overall survival， and objective response rate in patients with SCLC. In terms of safety， serplulimab combined with chemotherapy showed no significant difference in the incidence of ≥3 grade adverse events compared with chemotherapy alone in the treatment of SCLC， indicating a good safety profile； compared with combination therapies involving other immunosuppressive agents， the incidence rate of adverse events was also lower. In terms of cost-effectiveness， compared with chemotherapy alone， serplulimab combined with chemotherapy is not cost- effective， which may be related to the high price of serplulimab. CONCLUSIONS Serplulimab is effective and safe in the treatment of SCLC， but has no obvious advantage in terms of cost-effectiveness.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

The pathogenesis of neurodegenerative diseases (NDDs) is fundamentally linked to complex and profound alterations in metabolic networks within the brain, which exhibit marked spatial heterogeneity. While conventional bulk metabolomics is powerful for detecting global metabolic shifts, it inherently lacks spatial resolution. This methodological limitation hampers the ability to interrogate critical metabolic dysregulation within discrete anatomical brain regions and specific cellular microenvironments, thereby constraining a deeper understanding of the core pathological mechanisms that initiate and drive NDDs. To address this critical gap, spatial metabolomics, with mass spectrometry imaging (MSI) at its core, has emerged as a transformative approach. It uniquely overcomes the limitations of bulk methods by enabling high-resolution, simultaneous detection and precise localization of hundreds to thousands of endogenous molecules—including primary metabolites, complex lipids, neurotransmitters, neuropeptides, and essential metal ions—directly in situ from tissue sections. This powerful capability offers an unprecedented spatial perspective for investigating the intricate and heterogeneous chemical landscape of NDD pathology, opening new avenues for discovery. Accordingly, this review provides a comprehensive overview of the field, beginning with a discussion of the technical features, optimal application scenarios, and current limitations of major MSI platforms. These include the widely adopted matrix-assisted laser desorption/ionization (MALDI)-MSI, the ultra-high-resolution technique of secondary ion mass spectrometry (SIMS)-MSI, and the ambient ionization method of desorption electrospray ionization (DESI)-MSI, along with other emerging technologies. We then highlight the pivotal applications of spatial metabolomics in NDD research, particularly its role in elucidating the profound chemical heterogeneity within distinct pathological microenvironments. These applications include mapping unique molecular signatures around amyloid β‑protein (Aβ) plaques, uncovering the metabolic consequences of neurofibrillary tangles composed of hyperphosphorylated tau protein, and characterizing the lipid and metabolite composition of Lewy bodies. Moreover, we examine how spatial metabolomics contributes to constructing detailed metabolic vulnerability maps across the brain, shedding light on the biochemical factors that render certain neuronal populations and anatomical regions selectively susceptible to degeneration while others remain resilient. Looking beyond current applications, we explore the immense potential of integrating spatial metabolomics with other advanced research methodologies. This includes its combination with three-dimensional brain organoid models to recapitulate disease-relevant metabolic processes, its linkage with multi-organ axis studies to investigate how systemic metabolic health influences neurodegeneration, and its convergence with single-cell and subcellular analyses to achieve unprecedented molecular resolution. In conclusion, this review not only summarizes the current state and critical role of spatial metabolomics in NDD research but also offers a forward-looking perspective on its transformative potential. We envision its continued impact in advancing our fundamental understanding of NDDs and accelerating translation into clinical practice—from the discovery of novel biomarkers for early diagnosis to the development of high-throughput drug screening platforms and the realization of precision medicine for individuals affected by these devastating disorders.

[摘 要] 目的：探究组蛋白去乙酰化酶（HDAC）抑制剂帕比司他对黑色素瘤生长和免疫性的影响及其机制。方法：常规培养黑色素瘤细胞B16F0，用不同浓度的帕比司他处理细胞，WB法检测帕比司他对B16F0细胞中HDAC表达的影响，CCK-8法、划痕愈合实验、Transwell实验和流式细胞术分别检测帕比司他对B16F0细胞增殖、迁移和侵袭能力，以及细胞凋亡和周期的影响。转录组学检测帕比司他对B16F0细胞基因表达的影响，用qPCR法加以验证。流式细胞术检测帕比司他对B16F0细胞表面MHC Ⅰ/Ⅱ类分子表达的影响，B16F0与骨髓来源树突状细胞（BMDC）共培养检测帕比司他对BMDC细胞表达CD11c、CD80和CD86的影响，B16F0细胞移植瘤实验检测帕比司他对移植瘤生长和裸鼠免疫功能的影响。结果：帕比司他促进B16F0细胞中组蛋白3（H3）和α-微管蛋白（α-TUB）蛋白乙酰化（P < 0.01或P < 0.001或P < 0.000 1），抑制B16F0细胞增殖、迁移和侵袭能力，促进其凋亡，并使细胞周期阻滞于G1期（P < 0.05或P < 0.001或P < 0.000 1），促进B16F0细胞表面表达MHC Ⅰ/Ⅱ类分子表达并促进共培养BMDC成熟（均P < 0.01）。转录组学检测结果显示，帕比司他促进B16F0细胞中E-cadherin和抗原提呈相关基因的表达，抑制N-cadherin、vimentin、c-Myc和CDK1的表达，qPCR法验证了这些结果。帕比司他抑制裸鼠移植瘤的生长并增强荷瘤裸鼠的免疫功能（P < 0.05, P < 0.000 1）。结论：帕比司他可抑制B16F0细胞的恶性生物学行为，促进其凋亡，调控其免疫性，增强荷瘤裸鼠的免疫功能。

Objective To investigate the relationship between interleukin-1β (IL-1β) and miR-185-5p in the process of joint injury in acute gouty arthritis (AGA). Methods The serum miR-185-5p levels of 89 AGA patients and 91 healthy volunteers were detected by real-time quantitative PCR. The correlation between miR-185-5p expression level and VAS score or IL-1β expression level was evaluated by Pearson correlation coefficient method. Receiver operating characteristic (ROC) curve was used to evaluate the diagnostic value of miR-185-5p in AGA. THP-1 cells were induced by sodium urate (MSU) to construct an in vitro acute gouty inflammatory cell model. After the expression level of miR-185-5p in THP-1 cells was upregulated or downregulated by transfection of miR-185-5p mimics or inhibitors in vitro, inflammatory cytokines of THP-1 cells, such as IL-1β, IL-8 and tumor necrosis factor α (TNF-α), were detected by ELISA. The luciferase reporter gene assay was used to determine the interaction between miR-185-5p and the 3'-UTR of IL-1β. Results Compared with the healthy control group, the expression level of serum miR-185-5p in AGA patients was significantly reduced. The level of serum miR-185-5p was negatively correlated with VAS score and IL-1β expression level. The area under the curve (AUC) was 0.905, the sensitivity was 80.17% and the specificity was 83.52%. Down-regulation of miR-185-5p significantly promoted the expression of IL-1β, IL-8 and tumor necrosis factor (TNF-α), while overexpression of miR-185-5p showed the opposite results. Luciferase reporter gene assay showed that IL-1β was the target gene of miR-185-5p, and miR-185-5p negatively regulated the expression of IL-1β. Conclusion miR-185-5p alleviates the inflammatory response in AGA by inhibiting IL-1β.
Humans ; 3' Untranslated Regions ; Arthritis, Gouty/genetics* ; Interleukin-1beta/genetics* ; Interleukin-8 ; Luciferases ; MicroRNAs/genetics* ; Tumor Necrosis Factor-alpha

Objective To study the stress change characteristics of the cervical disc after removing different ranges of the uncinate process by establishing a three⁃dimensional finite element model of the C