The shared role of neutrophils in ankylosing spondylitis and ulcerative colitis.

This study aimed to analyze single-cell sequencing data to investigate immune cell interactions in ankylosing spondylitis (AS) and ulcerative colitis (UC). Vertebral bone marrow blood was collected from three AS patients for 10X single-cell sequencing. Analysis of single-cell data revealed distinct cell types in AS and UC patients. Cells significantly co-expressing immune cells (P < 0.05) were subjected to communication analysis. Overlapping genes of these co-expressing immune cells were subjected to GO and KEGG analyses. Key genes were identified using STRING and Cytoscape to assess their correlation with immune cell expression. The results showed the significance of neutrophils in both diseases (P < 0.01), with notable interactions identified through communication analysis. XBP1 emerged as a Hub gene for both diseases, with AUC values of 0.760 for AS and 0.933 for UC. Immunohistochemistry verified that the expression of XBP1 was significantly lower in the AS group and significantly greater in the UC group than in the control group (P < 0.01). This finding highlights the critical role of neutrophils in both AS and UC, suggesting the presence of shared immune response elements. The identification of XBP1 as a potential therapeutic target offers promising intervention avenues for both diseases.

Exploring the causal relationship between inflammatory cytokines and inflammatory arthritis: A Mendelian randomization study.

OBJECTIVES: Previous studies have reported an association between inflammatory cytokines and inflammatory arthritis, including Ankylosing spondylitis (AS), rheumatoid arthritis (RA), and psoriatic arthritis (PsA). This study aims to explore the causal relationship between inflammatory cytokines and AS, RA, and PsA using Mendelian randomization (MR). METHODS: We conducted a bidirectional two-sample MR analysis using genetic summary data from a publicly available genome-wide association study (GWAS) that included 41 genetic variations of inflammatory cytokines, as well as genetic variant data for AS, RA, and PsA from the FinnGen consortium. The main analysis method used was Inverse variance weighted (IVW) to investigate the causal relationship between exposure and outcome. Additionally, other methods such as MR Egger, weighted median (WM), simple mode, and weighted mode were employed to strengthen the final results. Sensitivity analysis was also performed to ensure the reliability of the findings. RESULTS: The results showed that macrophage colony-stimulating factor (MCSF) was associated with an increased risk of AS (OR = 1.163, 95 % CI = 1.016-1.33, p = 0.028). Conversely, high levels of TRAIL and beta nerve growth factor (ß-NGF) were associated with a decreased risk of AS (OR = 0.892, 95 % CI = 0.81-0.982, p = 0.002; OR = 0.829, 95 % CI = 0.696-0.988, p = 0.036). Four inflammatory cytokines were found to be associated with an increased risk of PsA: vascular endothelial growth factor (VEGF) (OR = 1.161, 95 % CI = 1.057-1.275, p = 0.002); Interleukin 12p70 (IL12p70) (OR = 1.189, 95 % CI = 1.049-1.346, p = 0.007); IL10 (OR = 1.216, 95 % CI = 1.024-1.444, p = 0.026); IL13 (OR = 1.159, 95 % CI = 1.05-1.28, p = 0.004). Interleukin 1 receptor antagonist (IL-1rα) was associated with an increased risk of seropositive RA (OR = 1.181, 95 % CI = 1.044-1.336, p = 0.008). Similarly, genetic susceptibility to inflammatory arthritis was found to be causally associated with multiple inflammatory cytokines. Lastly, the sensitivity analysis supported the robustness of these findings. CONCLUSIONS: This study provides additional insights into the relationship between inflammatory cytokines and inflammatory arthritis, and may offer new clues for the etiology, diagnosis, and treatment of inflammatory arthritis.

Weaker photosynthetic acclimation to fluctuating than to corresponding steady UVB radiation treatments in grapevines.

The effects of transient increases in UVB radiation on plants are not well known; whether cumulative damage dominates or, alternately, an increase in photoprotection and recovery periods ameliorates any negative effects. We investigated photosynthetic capacity and metabolite accumulation of grapevines (Vitis vinifera Cabernet Sauvignon) in response to UVB fluctuations under four treatments: fluctuating UVB (FUV) and steady UVB radiation (SUV) at similar total biologically effective UVB dose (2.12 and 2.23 kJ m-2 day-1), and their two respective no UVB controls. We found a greater decrease in stomatal conductance under SUV than FUV. There was no decrease in maximum yield of photosystem II (Fv/Fm) or its operational efficiency (ɸPSII) under the two UVB treatments, and Fv/Fm was higher under SUV than FUV. Photosynthetic capacity was enhanced under FUV in the light-limited region of rapid light-response curves but enhanced by SUV in the light-saturated region. Flavonol content was similarly increased by both UVB treatments. We conclude that, while both FUV and SUV effectively stimulate acclimation to UVB radiation at realistic doses, FUV confers weaker acclimation than SUV. This implies that recovery periods between transient increases in UVB radiation reduce UVB acclimation, compared to an equivalent dose of UVB provided continuously. Thus, caution is needed in interpreting the findings of experiments using steady UVB radiation treatments to infer effects in natural environments, as the stimulatory effect of steady UVB is greater than that of the equivalent fluctuating UVB.

Identification of novel protein biomarkers and therapeutic targets for ankylosing spondylitis using human circulating plasma proteomics and genome analysis.

The proteome serves as the primary basis for identifying targets for treatment. This study conducted proteomic range two-sample Mendelian randomization (MR) analysis to pinpoint potential protein markers and treatment targets for ankylosing spondylitis (AS). A total of 4907 data points on circulating protein expression were collected from a large-scale protein quantitative trait locus investigation involving 35,559 individuals. Using data from a Finnish study on AS as the outcome, the dataset comprised 166,144 individuals of European ancestry (1462 cases and 164,682 controls), and causal relationships were determined through bidirectional Mendelian randomization of two samples. Proteins were further validated and identified through single-cell expression analysis, certain cells showing enriched expression levels were detected, and possible treatment targets were optimized. Increased HERC5 expression predicted by genes was related to increased AS risk, whereas the expression of the remaining five circulating proteins, AIF1, CREB3L4, MLN, MRPL55, and SPAG11B, was negatively correlated with AS risk. For each increase in gene-predicted protein levels, the ORs of AS were 2.11 (95% CI 1.44-3.09) for HERC5, 0.14 (95% CI 0.05-0.41) for AIF1, 0.48 (95% CI 0.34-0.68) for CREB3L4, 0.54 (95% CI 0.42-0.68) for MLN, 0.23 (95% CI 0.13-0.38) for MRPL55, and 0.26 (95% CI 0.17-0.39) for SPAG11B. The hypothesis of a reverse causal relationship between these six circulating proteins and AS is not supported. Three of the six protein-coding genes were expressed in both the AS and healthy control groups, while CREB3L4, MLN, and SPAG11B were not detected. Increased levels of HERC5 predicted by genes are related to increased AS risk, whereas the levels of the remaining five circulating proteins, AIF1, CREB3L4, MLN, MRPL55, and SPAG11B, negatively correlate with AS risk. HERC5, AIF1, and MRPL55 are potential therapeutic targets for AS. This study advanced the field by employing a novel combination of proteomic range two-sample MR analysis and single-cell expression analysis to identify potential protein markers and therapeutic targets for AS. This approach enabled a comprehensive understanding of the causal relationships between circulating proteins and AS, which has not been extensively explored in previous studies.

Development and validation of a machine learning-based nomogram for predicting HLA-B27 expression.

BACKGROUND: HLA-B27 positivity is normal in patients undergoing rheumatic diseases. The diagnosis of many diseases requires an HLA-B27 examination. METHODS: This study screened totally 1503 patients who underwent HLA-B27 examination, liver/kidney function tests, and complete blood routine examination in First Affiliated Hospital of Guangxi Medical University. The training cohort included 509 cases with HLA-B27 positivity whereas 611 with HLA-B27 negativity. In addition, validation cohort included 147 cases with HLA-B27 positivity whereas 236 with HLA-B27 negativity. In this study, 3 ML approaches, namely, LASSO, support vector machine (SVM) recursive feature elimination and random forest, were adopted for screening feature variables. Subsequently, to acquire the prediction model, the intersection was selected. Finally, differences among 148 cases with HLA-B27 positivity and negativity suffering from ankylosing spondylitis (AS) were investigated. RESULTS: Six factors, namely red blood cell count, human major compatibility complex, mean platelet volume, albumin/globulin ratio (ALB/GLB), prealbumin, and bicarbonate radical, were chosen with the aim of constructing the diagnostic nomogram using ML methods. For training queue, nomogram curve exhibited the value of area under the curve (AUC) of 0.8254496, and C-value of the model was 0.825. Moreover, nomogram C-value of the validation queue was 0.853, and the AUC value was 0.852675. Furthermore, a significant decrease in the ALB/GLB was noted among cases with HLA-B27 positivity and AS cases. CONCLUSION: To conclude, the proposed ML model can effectively predict HLA-B27 and help doctors in the diagnosis of various immune diseases.

Application of machine learning in prediction of bone cement leakage during single-level thoracolumbar percutaneous vertebroplasty.

BACKGROUND: In the elderly, osteoporotic vertebral compression fractures (OVCFs) of the thoracolumbar vertebra are common, and percutaneous vertebroplasty (PVP) is a common surgical method after fracture. Machine learning (ML) was used in this study to assist clinicians in preventing bone cement leakage during PVP surgery. METHODS: The clinical data of 374 patients with thoracolumbar OVCFs who underwent single-level PVP at The First People's Hospital of Chenzhou were chosen. It included 150 patients with bone cement leakage and 224 patients without it. We screened the feature variables using four ML methods and used the intersection to generate the prediction model. In addition, predictive models were used in the validation cohort. RESULTS: The ML method was used to select five factors to create a Nomogram diagnostic model. The nomogram model's AUC was 0.646667, and its C value was 0.647. The calibration curves revealed a consistent relationship between nomogram predictions and actual probabilities. In 91 randomized samples, the AUC of this nomogram model was 0.7555116. CONCLUSION: In this study, we invented a prediction model for bone cement leakage in single-segment PVP surgery, which can help doctors in performing better surgery with reduced risk.

Deficit irrigation and leaf removal modulate anthocyanin and proanthocyanidin repartitioning of Cabernet Sauvignon (Vitis vinifera L.) grape and resulting wine profile.

BACKGROUND: In monsoonal climates, grape anthocyanin and proanthocyanidin (PA) accumulations are unsatisfactory for producing optimal wine. Agronomical practices are often considered to be effective means for regulating fruit components. However, there is a lack of quantitative information on the effects of deficit irrigation (DI), basal leaf removal (LR) or their combination of deficit irrigation and leaf removal (DILR) on the characteristics of anthocyanin and PA compositions and their implications on the resulting wine quality. In this study, the dynamics of grape anthocyanin and PA accumulation were investigated in DI, LR and DILR during grape ripening, and the resulting wine profile was assessed. RESULTS: The contents of reducing sugar and total anthocyanins in Cabernet Sauvignon berries were significantly increased by DI, LR and DILR, while titratable acidity, total flavan-3-ols and tannins levels were generally decreased. Notably, the levels of 3'5'-substituted anthocyanins, such as malvidin and its derivatives significantly increased, and 3'-substituted anthocyanins decreased in both grape and wine under DI and DILR strategies. Skin PAs were sensitive to water deficits, whereas they were insensitive to LR. In resulting wine, PAs content and the proportion of 3'-hydroxylated PAs, such as (+)-catechin, (-)-epicatechin and (-)-epicatechin-3-O-gallate units were significantly decreased under DI and DILR, while molecular mass and the proportion of 3'5'-hydroxylated units of PAs were increased in response to DILR. CONCLUSION: The DILR was the most favorable for the repartitioning of anthocyanin and PA metabolites, and promoted the accumulation of tri-substituted forms contributing a higher color intensity, mouthfeel persistence, structure, and astringency of wine. This information provides an important strategy for modulating the anthocyanin and PA compositions by agricultural practices and achieving the desired quality of grapes and wines in monsoonal climates. © 2021 Society of Chemical Industry.

EspF is crucial for Citrobacter rodentium-induced tight junction disruption and lethality in immunocompromised animals.

Attaching/Effacing (A/E) bacteria include human pathogens enteropathogenic Escherichia coli (EPEC), enterohemorrhagic E. coli (EHEC), and their murine equivalent Citrobacter rodentium (CR), of which EPEC and EHEC are important causative agents of foodborne diseases worldwide. While A/E pathogen infections cause mild symptoms in the immunocompetent hosts, an increasing number of studies show that they produce more severe morbidity and mortality in immunocompromised and/or immunodeficient hosts. However, the pathogenic mechanisms and crucial host-pathogen interactions during A/E pathogen infections under immunocompromised conditions remain elusive. We performed a functional screening by infecting interleukin-22 (IL-22) knockout (Il22-/-) mice with a library of randomly mutated CR strains. Our screen reveals that interruption of the espF gene, which encodes the Type III Secretion System effector EspF (E. coli secreted protein F) conserved among A/E pathogens, completely abolishes the high mortality rates in CR-infected Il22-/- mice. Chromosomal deletion of espF in CR recapitulates the avirulent phenotype without impacting colonization and proliferation of CR, and EspF complement in ΔespF strain fully restores the virulence in mice. Moreover, the expression levels of the espF gene are elevated during CR infection and CR induces disruption of the tight junction (TJ) strands in colonic epithelium in an EspF-dependent manner. Distinct from EspF, chromosomal deletion of other known TJ-damaging effector genes espG and map failed to impede CR virulence in Il22-/- mice. Hence our findings unveil a critical pathophysiological function for EspF during CR infection in the immunocompromised host and provide new insights into the complex pathogenic mechanisms of A/E pathogens.

Expression and localization of MrgprD in mouse intestinal tract.

MrgprD, a Mas-related G protein-coupled receptor, is initially identified in sensory neurons of mouse dorsal root ganglia (DRG) and has been suggested to participate in somatosensation. However, MrgprD has recently been found to be expressed outside the nervous system such as in aortic endothelia cells and neutrophils. In this study, we used immunohistochemistry to detect the expression and localization of MrgprD in mouse intestinal tract. The immunoreactivity (IR) of MrgprD was found in the smooth muscle layers of small intestine, colon and rectum. In addition, MrgprD IR was colocalized with F4/80-positive macrophages and CD3-positive T lymphocytes resident in the lamina propria of intestinal mucosa. MrgprD was also found to be expressed in primary peritoneal macrophages and splenic T lymphocytes. Furthermore, the presence of MrgprD mRNA and its protein was detected in murine macrophage-like RAW 264.7 and human T lymphocyte Jurkat cell lines. Our study shows, for the first time, the expression and localization of MrgprD in the intestinal tract and in macrophages and T lymphocytes, indicating the potential roles of MrgprD in intestinal mobility and immunity.

Decyanative Heteroarylations of Glycine Derivatives.

Amino acids and aromatic nitrogen heterocycles are widely used in pharmaceuticals. Herein, we present an effective visible-light-driven thiobenzoic acid (TBA)-catalyzed decyanative C(sp3)-H heteroarylation of glycine derivatives. This process occurs under mild and straightforward conditions, affording a range of valuable yet challenging-to-obtain α-heteroaryl amino acid derivatives. Moreover, this organocatalytic C(sp3)-C(sp2) bond formation reaction is applicable to the late-stage modification of various short peptides.

Construction of a predictive model for blood transfusion in patients undergoing total hip arthroplasty and identification of clinical heterogeneity.

A precise forecast of the need for blood transfusions (BT) in patients undergoing total hip arthroplasty (THA) is a crucial step toward the implementation of precision medicine. To achieve this goal, we utilized supervised machine learning (SML) techniques to establish a predictive model for BT requirements in THA patients. Additionally, we employed unsupervised machine learning (UML) approaches to identify clinical heterogeneity among these patients. In this study, we recruited 224 patients undergoing THA. To identify factors predictive of BT during the perioperative period of THA, we employed LASSO regression and the random forest (RF) algorithm as part of supervised machine learning (SML). Using logistic regression, we developed a predictive model for BT in THA patients. Furthermore, we utilized unsupervised machine learning (UML) techniques to cluster THA patients who required BT based on similar clinical features. The resulting clusters were subsequently visualized and validated. We constructed a predictive model for THA patients who required BT based on six predictive factors: Age, Body Mass Index (BMI), Hemoglobin (HGB), Platelet (PLT), Bleeding Volume, and Urine Volume. Before surgery, 1 h after surgery, 1 day after surgery, and 1 week after surgery, significant differences were observed in HGB and PLT levels between patients who received BT and those who did not. The predictive model achieved an AUC of 0.899. Employing UML, we identified two distinct clusters with significantly heterogeneous clinical characteristics. Age, BMI, PLT, HGB, bleeding volume, and urine volume were found to be independent predictors of BT requirement in THA patients. The predictive model incorporating these six predictors demonstrated excellent predictive performance. Furthermore, employing UML enabled us to classify a heterogeneous cohort of THA patients who received BT in a meaningful and interpretable manner.

Photoredox/NHC Dual Catalysis Enabled de Novo Synthesis of α-Amino Acids Derivatives.

Herein, we report a mild and operationally simple photoredox/NHC dual catalysis strategy for the α-carboxylation of tertiary amine C(sp3)-H bonds using diethyl pyrocarbonate. This method offers a novel approach for synthesizing α-amino acid derivatives. The protocol features a broad substrate scope, accommodating both N-aryl tetrahydroisoquinolines (THIQ) and N-methyl aniline and is scalable to gram quantities. Additionally, it is suitable for the late-stage derivatization of certain pharmaceutical compounds.

Artificial intelligence-driven multiomics predictive model for abdominal aortic aneurysm subtypes to identify heterogeneous immune cell infiltration and predict disease progression.

BACKGROUND: Abdominal aortic aneurysm (AAA) poses a significant health risk and is influenced by various compositional features. This study aimed to develop an artificial intelligence-driven multiomics predictive model for AAA subtypes to identify heterogeneous immune cell infiltration and predict disease progression. Additionally, we investigated neutrophil heterogeneity in patients with different AAA subtypes to elucidate the relationship between the immune microenvironment and AAA pathogenesis. METHODS: This study enrolled 517 patients with AAA, who were clustered using k-means algorithm to identify AAA subtypes and stratify the risk. We utilized residual convolutional neural network 200 to annotate and extract contrast-enhanced computed tomography angiography images of AAA. A precise predictive model for AAA subtypes was established using clinical, imaging, and immunological data. We performed a comparative analysis of neutrophil levels in the different subgroups and immune cell infiltration analysis to explore the associations between neutrophil levels and AAA. Quantitative polymerase chain reaction, Western blotting, and enzyme-linked immunosorbent assay were performed to elucidate the interplay between CXCL1, neutrophil activation, and the nuclear factor (NF)-κB pathway in AAA pathogenesis. Furthermore, the effect of CXCL1 silencing with small interfering RNA was investigated. RESULTS: Two distinct AAA subtypes were identified, one clinically more severe and more likely to require surgical intervention. The CNN effectively detected AAA-associated lesion regions on computed tomography angiography, and the predictive model demonstrated excellent ability to discriminate between patients with the two identified AAA subtypes (area under the curve, 0.927). Neutrophil activation, AAA pathology, CXCL1 expression, and the NF-κB pathway were significantly correlated. CXCL1, NF-κB, IL-1ß, and IL-8 were upregulated in AAA. CXCL1 silencing downregulated NF-κB, interleukin-1ß, and interleukin-8. CONCLUSION: The predictive model for AAA subtypes demonstrated accurate and reliable risk stratification and clinical management. CXCL1 overexpression activated neutrophils through the NF-κB pathway, contributing to AAA development. This pathway may, therefore, be a therapeutic target in AAA.

Prediction of additional hospital days in patients undergoing cervical spine surgery with machine learning methods.

BACKGROUND: Machine learning (ML), a subset of artificial intelligence (AI), uses algorithms to analyze data and predict outcomes without extensive human intervention. In healthcare, ML is gaining attention for enhancing patient outcomes. This study focuses on predicting additional hospital days (AHD) for patients with cervical spondylosis (CS), a condition affecting the cervical spine. The research aims to develop an ML-based nomogram model analyzing clinical and demographic factors to estimate hospital length of stay (LOS). Accurate AHD predictions enable efficient resource allocation, improved patient care, and potential cost reduction in healthcare. METHODS: The study selected CS patients undergoing cervical spine surgery and investigated their medical data. A total of 945 patients were recruited, with 570 males and 375 females. The mean number of LOS calculated for the total sample was 8.64 ± 3.7 days. A LOS equal to or <8.64 days was categorized as the AHD-negative group (n = 539), and a LOS > 8.64 days comprised the AHD-positive group (n = 406). The collected data was randomly divided into training and validation cohorts using a 7:3 ratio. The parameters included their general conditions, chronic diseases, preoperative clinical scores, and preoperative radiographic data including ossification of the anterior longitudinal ligament (OALL), ossification of the posterior longitudinal ligament (OPLL), cervical instability and magnetic resonance imaging T2-weighted imaging high signal (MRI T2WIHS), operative indicators and complications. ML-based models like Lasso regression, random forest (RF), and support vector machine (SVM) recursive feature elimination (SVM-RFE) were developed for predicting AHD-related risk factors. The intersections of the variables screened by the aforementioned algorithms were utilized to construct a nomogram model for predicting AHD in patients. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve and C-index were used to evaluate the performance of the nomogram. Calibration curve and decision curve analysis (DCA) were performed to test the calibration performance and clinical utility. RESULTS: For these participants, 25 statistically significant parameters were identified as risk factors for AHD. Among these, nine factors were obtained as the intersection factors of these three ML algorithms and were used to develop a nomogram model. These factors were gender, age, body mass index (BMI), American Spinal Injury Association (ASIA) scores, magnetic resonance imaging T2-weighted imaging high signal (MRI T2WIHS), operated segment, intraoperative bleeding volume, the volume of drainage, and diabetes. After model validation, the AUC was 0.753 in the training cohort and 0.777 in the validation cohort. The calibration curve exhibited a satisfactory agreement between the nomogram predictions and actual probabilities. The C-index was 0.788 (95% confidence interval: 0.73214-0.84386). On the decision curve analysis (DCA), the threshold probability of the nomogram ranged from 1 to 99% (training cohort) and 1 to 75% (validation cohort). CONCLUSION: We successfully developed an ML model for predicting AHD in patients undergoing cervical spine surgery, showcasing its potential to support clinicians in AHD identification and enhance perioperative treatment strategies.

Characterization of volatile compounds and sensory properties of spine grape (Vitis davidii Foex) brandies aged with different toasted wood chips.

Aging is an important process for improving wine and brandy quality. In this study, the chemical characterization and sensory properties of spine grape brandies were compared after aging with various species of wood chips, including French oak (FO), American oak (AO), Mongolian oak (MO), Japanese blue oak (JO), chestnut, catalpa, and cherry. The results showed that high color intensity and significant concentrations of tannins and polyphenols were observed in the brandies aged with FO, AO, and chestnut chips. The volatile compounds, such as ethyl decanoate, ethyl 2-methylbutanoate, ethyl octanoate, methyl salicylate, (Z)-2-hexenol, and furfural, contributed to the floral, fruity, and roasted/smoky attributes of the brandies aged with FO, AO, and chestnut chips. The 1-butanol, 1-propanol, phenylethanol, phenylethyl acetate, isoamyl acetate, and linalool contributed to the fruity, honey, and floral attributes of the brandies aged with JO and cherry chips. These findings are extremely useful for the production of differentiated and high-quality spine grape brandies.

Development and validation of a diagnostic model to differentiate spinal tuberculosis from pyogenic spondylitis by combining multiple machine learning algorithms.

This study focused on the development and validation of a diagnostic model to differentiate between spinal tuberculosis (STB) and pyogenic spondylitis (PS). We analyzed a total of 387 confirmed cases, out of which 241 were diagnosed with STB and 146 were diagnosed with PS. These cases were randomly divided into a training group (n = 271) and a validation group (n = 116). Within the training group, four machine learning (ML) algorithms (least absolute shrinkage and selection operator [LASSO], logistic regression analysis, random forest, and support vector machine recursive feature elimination [SVM-RFE]) were employed to identify distinctive variables. These specific variables were then utilized to construct a diagnostic model. The model's performance was subsequently assessed using the receiver operating characteristic (ROC) curves and the calibration curves. Finally, internal validation of the model was undertaken in the validation group. Our findings indicate that PS patients had an average platelet-to-neutrophil ratio (PNR) of 277.86, which was significantly higher than the STB patients' average of 69.88. The average age of PS patients was 54.71 years, older than the 48 years recorded for STB patients. Notably, the neutrophil-to-lymphocyte ratio (NLR) was higher in PS patients at 6.15, compared to the 3.46 NLR in STB patients. Additionally, the platelet volume distribution width (PDW) in PS patients was 0.2, compared to 0.15 in STB patients. Conversely, the mean platelet volume (MPV) was lower in PS patients at an average of 4.41, whereas STB patients averaged 8.31. Hemoglobin (HGB) levels were lower in PS patients at an average of 113.31 compared to STB patients' average of 121.64. Furthermore, the average red blood cell (RBC) count was 4.26 in PS patients, which was less than the 4.58 average observed in STB patients. After evaluation, seven key factors were identified using the four ML algorithms, forming the basis of our diagnostic model. The training and validation groups yielded area under the curve (AUC) values of 0.841 and 0.83, respectively. The calibration curves demonstrated a high alignment between the nomogram-predicted values and the actual measurements. The decision curve indicated optimal model performance with a threshold set between 2% and 88%. In conclusion, our model offers healthcare practitioners a reliable tool to efficiently and precisely differentiate between STB and PS, thereby facilitating swift and accurate diagnoses.

Integrating multiomics and Single-Cell communication analysis to uncover Ankylosing spondylitis mechanisms.

BACKGROUND: Ankylosing spondylitis (AS) is a chronic inflammatory joint disorder, necessitating early diagnosis and effective treatment. The specific mechanism of action of Cassia twigs in the treatment of AS is not fully understood. METHODS: Blood samples and clinical data from 28,458 individuals (6,101 with AS, 22,357 without AS) were collected. To construct a predictive model, we utilized logistic regressions and machine learning techniques to create a dynamic nomogram. Immune cell infiltration was evaluated using the GSE73754 dataset. Subsequently, we obtained vertebral bone marrow blood from AS patients for 10X single-cell sequencing. We also extracted and purified total RNA from hip joint ligament tissue samples from six AS patients and six non-AS patients. The genes related to the expression of AS and Cassia twigs were analyzed comprehensively, and the specific drug targets were identified by molecular docking. The interactions between immune cells through cell communication analysis were elucidated. RESULTS: We developed a dynamic nomogram incorporating the neutrophil count (NEUT) and other variables. Neutrophil immune responses were confirmed through immune infiltration analysis utilizing GSE73754. We observed the early involvement of neutrophils in the pathology of AS. The CAT-expressing Cassia twigs gene could be used as a drug target for the treatment of AS. Moreover, comprehensive RNA analysis revealed notable CAT expression in neutrophils and various other immune cells. CONCLUSIONS: Neutrophils play dual roles in AS, regulating inflammation and initiating differentiation signals to other cells. The CAT gene, which is expressed in Cassia twigs, has emerged as a potential therapeutic target for AS treatment.

CT Anatomical Analysis of C4 Pedicle and Lateral Mass in Children Aged 0-14 in Southern China.

OBJECTIVE: The C4 is the transition point between the upper and lower cervical vertebrae and plays a pivotal role in the middle of the cervical spine. Currently, there are limited reports on large-scale sample studies regarding C4 anatomy in children, and a scarcity of experience exists in pediatric cervical spine surgery. The current study addresses the dearth of anatomical measurements of the C4 vertebral arch and lateral mass in a substantial sample of children. This study aims to measure the imaging anatomy of the C4 vertebral arch and lateral mass in children under 14 years of age across various age groups, investigate the growth and development of these structures. METHODS: We measured 12 indicators, including the size (D1, D2, D3, D4, D5, D6, D7, and D8) and angle (A, C, D, and E) of the C4 vertebral arch and lateral mass, in 513 children who underwent cervical CT examinations at our hospital. We employed the aggregate function for statistical analysis, conducted t-tests for difference statistics, and utilized the least squares method for regression analysis. RESULTS: Overall, as age increased, there was a gradual increase in the size of the vertebral arch and lateral mass. Additionally, the medial inclination angle of the vertebral arch decreased, and the lateral mass flattened gradually. The rate of change decreased gradually with age. The mean value of D1 increased from 2.31 mm to 3.88 mm, of D2 from 16.75 mm to 29.2 mm, of D3 from 2.21 mm to 4.92 mm, and of D4 from 7.34 mm to 11.84 mm. Meanwhile, the mean value of D5 increased from 5.2 mm to 9.71 mm, of D6 from 10.19 mm to 16.16 mm, of D7 from 2.53 mm to 5.67 mm, and of D8 from 6.11 mm to 11.45 mm. Angle A ranged from 49.12° to 54.97°, angle C from 15.28° to 19.83°, angle D from 39.91° to 53.7°, and angle E from 18.63° to 28.08°. CONCLUSION: Prior to cervical spine surgery in children, meticulous CT imaging anatomical measurements is essential. The imaging data serves as a reference for posterior C4 internal fixation, aids in designing posterior cervical screws for pediatric patients, and offer morphological anatomical references for posterior cervical spine surgery and screw design in pediatric patients.

Prediction model for spinal cord injury in spinal tuberculosis patients using multiple machine learning algorithms: a multicentric study.

Spinal cord injury (SCI) is a prevalent and serious complication among patients with spinal tuberculosis (STB) that can lead to motor and sensory impairment and potentially paraplegia. This research aims to identify factors associated with SCI in STB patients and to develop a clinically significant predictive model. Clinical data from STB patients at a single hospital were collected and divided into training and validation sets. Univariate analysis was employed to screen clinical indicators in the training set. Multiple machine learning (ML) algorithms were utilized to establish predictive models. Model performance was evaluated and compared using receiver operating characteristic (ROC) curves, area under the curve (AUC), calibration curve analysis, decision curve analysis (DCA), and precision-recall (PR) curves. The optimal model was determined, and a prospective cohort from two other hospitals served as a testing set to assess its accuracy. Model interpretation and variable importance ranking were conducted using the DALEX R package. The model was deployed on the web by using the Shiny app. Ten clinical characteristics were utilized for the model. The random forest (RF) model emerged as the optimal choice based on the AUC, PRs, calibration curve analysis, and DCA, achieving a test set AUC of 0.816. Additionally, MONO was identified as the primary predictor of SCI in STB patients through variable importance ranking. The RF predictive model provides an efficient and swift approach for predicting SCI in STB patients.

Elucidating the causal nexus between antibody-mediated immunity and autoimmune diseases: Insights from bidirectional mendelian randomization, gene expression profiling, and drug sensitivity analysis.

OBJECTIVE: This study aimed to elucidate the causal relationships between antibodies and autoimmune diseases using Mendelian randomization (MR). METHODS: Data on 46 antibodies were obtained from genome-wide association studies (GWAS). Autoimmune disease data were sourced from the FinnGen consortium and the IEU OpenGWAS project. Inverse-variance weighted (IVW) analysis was the primary method, supplemented by heterogeneity and sensitivity analyses. We also examined gene expression near significant SNPs and conducted drug sensitivity analyses. RESULTS: Antibodies and autoimmune diseases exhibit diverse interactions. Antibodies produced after Polyomavirus infection tend to increase the risk of several autoimmune diseases, while those following Human herpesvirus 6 infection generally reduce it. The impact of Helicobacter pylori infection varies, with different antibodies affecting autoimmune diseases in distinct ways. Overall, antibodies significantly influence the risk of developing autoimmune diseases, whereas autoimmune diseases have a lesser impact on antibody levels. Gene expression and drug sensitivity analyses identified multiple genes and drugs as potential treatment options for ankylosing spondylitis (AS), with the AIF1 gene being particularly promising. CONCLUSIONS: Bidirectional MR analysis confirms complex causal relationships between various antibodies and autoimmune diseases, revealing intricate patterns of post-infection antibody interactions. Several drugs and genes, notably AIF1, show potential as candidates for AS treatment, offering new avenues for research. Further exploration of the underlying mechanisms is necessary.