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.

Electrochemical Oxidative 1,2-Dithiocyanation: Access to Functionalized Alkenes and Alkynes.

Reported herein is the 1,2-dithiocyanation of alkenes and alkynes via an efficient and facile electrochemical method. This approach not only showed a broad substrate scope and good functional-group compatibility but also avoided stoichiometric oxidants. Different from previous reports, various internal alkynes could be tolerated to provide tetra-substituted alkenes. Further gram-scale-up experiments and synthetic transformation demonstrated a potential application in organic synthesis. This process underwent a radical pathway, as evidenced by our mechanistic studies.

Photoinduced Catalyst-Free Deuterodefunctionalization of Aryltriazenes with CDCl3.

A photoinduced deuterodetriazenation of aryltriazenes with CDCl3 under catalyst-free conditions is reported. The reactions featured simple operation, ecofriendly conditions, readily available reagents, inexpensive D sources, precise site selectivity, and a wide range of substrates. Since aryltriazenes could be readily synthesized from arylamine, this protocol can be used as a general method for easily and accurately incorporating deuterium into aromatic systems by using CDCl3 as a D source.

Visible-Light-Driven Bifunctional Photocatalytic Radical-Cascade Selenocyanation/Cyclization of Acrylamides with KSeCN.

A visible-light-induced radical-cascade selenocyanation/cyclization of N-alkyl-N-methacryloyl benzamides, 2-aryl-N-acryloyl indoles, and N-methacryloyl-2-phenylbenzimidazoles with potassium isoselenocyanate (KSeCN) was developed. The reactions were carried out with inexpensive KSeCN as a selenocyanation reagent, potassium persulfate as an oxidant, 2,4,6-triphenylpyrylium tetrafluoroborate as a bifunctional catalyst for phase-transfer catalysis, and photocatalysis. A library of selenocyanate-containing isoquinoline-1,3(2H,4H)-diones, indolo[2,1-a]isoquinoline-6(5H)-ones, and benzimidazo[2,1-a]isoquinolin-6(5H)-ones were achieved in moderate to excellent yields at room temperature under visible-light and ambient conditions. Importantly, the present protocol features mild reaction conditions, large-scale synthesis, simple manipulation, product derivatization, good functional group, and heterocycle tolerance.

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.

Visible-Light-Promoted and EDA Complex-Driven [4 + 2] Annulation for the Construction of Naphtho[1',2':4,5]imidazo[1,2-a]pyridines.

A green visible-light-promoted and electron donor-acceptor (EDA) complex-driven synthetic strategy for the construction of value-added naphtho[1',2':4,5]imidazo[1,2-a]pyridines from 2-arylimidazo[1,2-a]pyridines with Z-α-bromocinnamaldehydes has been accomplished under photocatalyst- and transition-metal-free conditions. This efficient annulation approach provides a new and straightforward pathway for the annulative π-extension of imidazo[1,2-a]pyridine-based aromatics. Moreover, the sustainable methodology exhibits simple operation, a wide range of substrates, benign conditions, and good functional group compatibility.

Curcumin attenuates aflatoxin B1-induced ileum injury in ducks by inhibiting NLRP3 inflammasome and regulating TLR4/NF-κB signaling pathway.

Aflatoxin B1 (AFB1) is a widespread toxic contamination in feed for animals. The primary active component of turmeric, curcumin (Cur), is an antioxidant and an anti-inflammatory. However, it is yet unknown how AFB1 affects the intestinal epithelial barrier and whether Cur acts as a protective mechanism when exposed to AFB1. Here, we explored the mechanism of AFB1-induced intestinal injury from intestinal epithelial barrier, inflammation, pyroptosis, and intestinal flora, and evaluated the protective role of Cur. We found that AFB1 caused weight loss and intestinal morphological damage that is mainly characterized by shortened intestinal villi, deepened crypts, and damaged intestinal epithelium. Exposure to AFB1 decreased the expression of Claudin-1, MUC2, ZO-1, and Occludin and increased the expression of pyroptosis-related factors (NLRP3, GSDMD, Caspase-1, IL-1ß, and IL-18) and inflammation-related factors (TLR4, NF-κB, IκB, IFN-γ, and TNF-α). Furthermore, ileal gut microbiota was altered, and simultaneously, the Lactobacillus abundance was decreased. The gut microbiota interacts with a wide range of physiologic functions and disease development in the host through its metabolites, and disturbances in gut microbial metabolism can cause functional impairment of the ileum. Meanwhile, Cur can ameliorate histological ileum injuries and intestinal flora disturbance caused by AFB1. We found that Cur reversed the effects of AFB1 through modulating both NLRP3 inflammasome and the TLR4/NF-κB signaling pathway. In conclusion, AFB1 can induce inflammatory damage and pyroptosis in duck ileum, while Cur has obviously protective effects on all the above damages.

Pd-Catalyzed Direct C7 Trifluoromethylation of Indolines with Umemoto's Reagent.

An efficient palladium-catalyzed region-selective C7-trifluoromethylation of indolines using commercially available Umemoto's reagent was reported. The reaction utilizing Umemoto's reagent as CF3 radical precursor, pyrimidine as a removable directing group, Pd(II) as a catalyst, and Cu(II) as an oxidant furnished the required products with excellent regioselectivities and good yields. The present strategy has good region-selectivity, broad substrate scope, and scale-up application. Additionally, the present method was underlined by the direct C-1 trifluoromethylation of carbazoles. Furthermore, C7 trifluoromethylated indole can also be easily obtained via Pd-catalyzed direct C-7 trifluoromethylation/oxidation/deprotection sequential reactions.

Causal relationship between systemic lupus erythematosus and primary liver cirrhosis based on two-sample bidirectional Mendelian randomization and transcriptome overlap analysis.

BACKGROUND: Overlapping cases of systemic lupus erythematosus (SLE) and primary biliary cirrhosis (PBC) are rare and have not yet been fully proven to be accidental or have a common genetic basis. METHODS: Two-sample bidirectional Mendelian randomization (MR) analysis was applied to explore the potential causal relationship between SLE and PBC. The heterogeneity and reliability of MR analysis were evaluated through Cochran's Q-test and sensitivity test, respectively. Next, transcriptome overlap analysis of SLE and PBC was performed using the Gene Expression Omnibus database to identify the potential mechanism of hub genes. Finally, based on MR analysis, the potential causal relationship between hub genes and SLE or PBC was validated again. RESULTS: The MR analysis results indicated that SLE and PBC were both high-risk factors for the occurrence and development of the other party. On the one hand, MR analysis had heterogeneity, and on the other hand, it also had robustness. Nine hub genes were identified through transcriptome overlap analysis, and machine learning algorithms were used to verify their high recognition efficiency for SLE patients. Finally, based on MR analysis, it was verified that there was no potential causal relationship between the central gene SOCS3 and SLE, but it was a high-risk factor for the potential risk of PBC. CONCLUSION: The two-sample bidirectional MR analysis revealed that SLE and PBC were high-risk factors for each other, indicating that they had similar genetic bases, which could to some extent overcome the limitation of insufficient overlap in case samples of SLE and PBC. The analysis of transcriptome overlapping hub genes provided a theoretical basis for the potential mechanisms and therapeutic targets of SLE with PBC overlapping cases.

Arsenic and polystyrene-nano plastics co-exposure induced testicular toxicity: Triggers oxidative stress and promotes apoptosis and inflammation in mice.

Co-existing of polystyrene-nano plastics (PSNPs) and arsenic (As) in the environment caused a horrendous risk to human health. However, the potential mechanism of PSNPs and As combination induced testicular toxicity in mammals has not been elucidated. Therefore, we first explore the testicular toxicity and the potential mechanism in male Kunming mice exposed to As or/and PSNPs. Results revealed that compared to the As or PSNPs group, the combined group showed more significant testicular toxicity. Specifically, As and PSNPs combination induced irregular spermatozoa array and blood-testis barrier disruption. Simultaneously, As and PSNPs co-exposure also exacerbated oxidative stress, including increasing the MDA content, and down-regulating expression of Nrf-2, HO-1, SOD-1, and Trx. PSNPs and As combination also triggered testicular apoptosis, containing changes in apoptotic factors (P53, Bax, Bcl-2, Cytc, Caspase-8, Caspase-9, and Caspase-3). Furthermore, co-exposed to As and PSNPs aggravated inflammatory damage characterized by targeted phosphorylation of NF-κB and degradation of I-κB. In summary, our results strongly confirmed As + PSNPs co-exposure induced the synergistic toxicity of testis through excessive oxidative stress, apoptosis, and inflammation, which could offer a new sight into the mechanism of environmental pollutants co-exposure induced male reproductive toxicity.

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.

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.

Arsenic induced neurotoxicity in the brain of ducks: The potential involvement of the gut-brain axis.

BACKGROUND: Arsenic is a widely distributed ecotoxic pollutant that has been found to cause neurotoxicity in a variety of species. Gut-brain axis is a two-way information network between the gut microbiome and the brain, which is closely related to organismal health. However, the role of the gut-brain axis in arsenic-induced neurotoxicity remains largely unknown. METHODS: In order to explore whether there is a relationship between brain and gut microbiota of meat ducks, we performed molecular biological detection including RT-qPCR and Western blot, as well as morphological detection including, HE staining and immunohistochemistry. Meanwhile, intestinal contents were analyzed using 16 S ribosomal RNA gene sequencing and analysis RESULTS: In this study, we investigated whether arsenic trioxide (ATO) can activate the gut microbiome-brain axis to induce intestinal and brain injury. The results showed that ATO-exposure disrupted the diversity balance of intestinal microbiota and integrity and injured the intestinal structure. ATO-exposure also reduced the number of glycogen and goblet cells in the duodenum. In addition, exposure to ATO caused intestinal inflammatory injury by activating NF-κB signaling pathway and promoting the expression of its target genes. Meanwhile, the tight junction-related proteins (ZO-1, occludin) of gut and brain were reduced by ATO exposure. Furthermore, results also revealed that ATO-exposure induced brain injury, including neuronal cell vacuolization and reduced numbers of neuronal cells in the cortex and hippocampus. Remarkably, ATO-exposure also disrupted neurotransmitter levels. Additionally, our further molecular mechanism study revealed that ATO-exposure increased the expression of autophagy and apoptosis related mRNA and proteins levels in the brain tissues. CONCLUSION: Altogether, these findings provide a new insight into that ATO-exposure induced intestinal injury and aggravated neurotoxicity via the gut-brain axis.

Mitopherogenesis, a form of mitochondria-specific ectocytosis, regulates sperm mitochondrial quantity and fertility.

Mitochondrial export into the extracellular space is emerging as a fundamental cellular process implicated in diverse physiological activities. Although a few studies have shed light on the process of discarding damaged mitochondria, how mitochondria are exported and the functions of mitochondrial release remain largely unclear. Here we describe mitopherogenesis, a formerly unknown process that specifically secretes mitochondria through a unique extracellular vesicle termed a 'mitopher'. We observed that during sperm development in male Caenorhabditis elegans, healthy mitochondria are exported out of the spermatids through mitopherogenesis and each of the generated mitophers harbours only one mitochondrion. In mitopherogenesis, the plasma membrane first forms mitochondrion-embedding outward buds, which then promptly bud off and thereby result in the generation of mitophers. Mechanistically, extracellular protease signalling in the testis triggers mitopher formation from spermatids, which is partially mediated by the tyrosine kinase SPE-8. Moreover, mitopherogenesis requires normal microfilament dynamics, whereas myosin VI antagonizes mitopher generation. Strikingly, our three-dimensional electron microscopy analyses indicate that mitochondrial quantity requires precise modulation during sperm development, which is critically mediated by mitopherogenesis. Inhibition of mitopherogenesis causes accumulation of mitochondria in sperm, which may lead to sperm motility and fertility defects. Our findings identify mitopherogenesis as a previously undescribed process for mitochondria-specific ectocytosis, which may represent a fundamental branch of mechanisms underlying mitochondrial quantity control to regulate cell functions during development.

Network pharmacology combined with molecular docking and experimental validation to explore the potential mechanism of Cinnamomi ramulus against ankylosing spondylitis.

BACKGROUND: Cinnamomi ramulus (C. ramulus) is frequently employed in the treatment of ankylosing spondylitis (AS). However, the primary constituents, drug targets, and mechanisms of action remain unidentified. METHODS: In this study, various public databases and online tools were employed to gather information on the compounds of C. ramulus, drug targets, and disease targets associated with ankylosing spondylitis. The intersection of drug targets and disease targets was then determined to identify the common targets, which were subsequently used to construct a protein-protein interaction (PPI) network using the STRING database. Network analysis and the analysis of hub genes and major compounds were conducted using Cytoscape software. Furthermore, the Metascape platform was utilized for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. Molecular docking studies and immunohistochemical experiments were performed to validate the core targets. RESULTS: The network analysis identified 2-Methoxycinnamaldehyde, cinnamaldehyde, and 2-Hydroxycinnamaldehyde as the major effective compounds present in C. ramulus. The PPI network analysis revealed PTGS2, MMP9, and TLR4 as the most highly correlated targets. GO and KEGG analyses indicated that C. ramulus exerts its therapeutic effects in ankylosing spondylitis through various biological processes, including the response to hormones and peptides, oxidative stress response, and inflammatory response. The main signaling pathways involved were IL-17, TNF, NF-kappa B, and Toll-like receptor pathways. Molecular docking analysis confirmed the strong affinity between the key compounds and the core targets. Additionally, immunohistochemical analysis demonstrated an up-regulation of PTGS2, MMP9, and TLR4 levels in ankylosing spondylitis. CONCLUSIONS: This study provides insights into the effective compounds, core targets, and potential mechanisms of action of C. ramulus in the treatment of ankylosing spondylitis. These findings establish a solid groundwork for future fundamental research in this field.

Selective Oxidative Methyl C-H Functionalization of Butylated Hydroxytoluene toward Arylimines/N-Heterocycles.

A metal-free and selective oxidative methyl C-H functionalization of BHT with aniline compounds has been developed. This innovative method enables the facile and efficient synthesis of a diverse array of BHT-functionalized N-containing skeletons, including arylamines, benzoxazoles, benzothiazoles, benzimidazoles, quinazolines, and quinazolinones, all of which are challenging to access. The control experiment involving TEMP18O suggests that the radical adduct of TEMPO with the benzyl radical of BHT may serve as an intermediate.

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.

The causal relationship between autoimmune diseases and osteoporosis: a study based on Mendelian randomization.

Objective: The relationship between different autoimmune diseases and bone mineral density (BMD) and fractures has been reported in epidemiological studies. This study aimed to explore the causal relationship between autoimmune diseases and BMD, falls, and fractures using Mendelian randomization (MR). Methods: The instrumental variables were selected from the aggregated statistical data of these diseases from the largest genome-wide association study in Europe. Specifically, 12 common autoimmune diseases were selected as exposure. Outcome variables included BMD, falls, and fractures. Multiple analysis methods were utilized to comprehensively evaluate the causal relationship between autoimmune diseases and BMD, falls, and fractures. Additionally, sensitivity analyses, including Cochran's Q test, MR-Egger intercept test, and one analysis, were conducted to verify the result's reliability. Results: Strong evidence was provided in the results of the negatively association of ulcerative colitis (UC) with forearm BMD. UC also had a negatively association with the total body BMD, while inflammatory bowel disease (IBD) depicted a negatively association with the total body BMD at the age of 45-60 years. Horizontal pleiotropy or heterogeneity was not detected through sensitivity analysis, indicating that the causal estimation was reliable. Conclusion: This study shows a negative causal relationship between UC and forearm and total body BMD, and between IBD and total body BMD at the age of 45-60 years. These results should be considered in future research and when public health measures and osteoporosis prevention strategies are formulated.

Using Machine Learning to Predict Surgical Site Infection After Lumbar Spine Surgery.

Objective: The objective of this study was to utilize machine learning techniques to analyze perioperative factors and identify blood glucose levels that can predict the occurrence of surgical site infection following posterior lumbar spinal surgery. Methods: A total of 4019 patients receiving lumbar internal fixation surgery from an institute were enrolled between June 2012 and February 2021. First, the filtered data were randomized into the test and verification groups. Second, in the test group, specific variables were screened using logistic regression analysis, Lasso regression analysis, support vector machine, and random forest. Specific variables obtained using the four methods were intersected, and a dynamic model was constructed. ROC and calibration curves were constructed to assess model performance. Finally, internal model performance was verified in the verification group using ROC and calibration curves. Results: The data from 4019 patients were collected. In total, 1327 eligible cases were selected. By combining logistic regression analysis with three machine learning algorithms, this study identified four predictors associated with SSI, namely Modic changes, sebum thickness, hemoglobin, and glucose. Using this information, a prediction model was developed and visually represented. Then, we constructed ROC and calibration curves using the test group; the area under the ROC curve was 0.988. Further, calibration curve analysis revealed favorable consistency of nomogram-predicted values compared with real measurements. The C-index of our model was 0.986 (95% CI 0.981-0.994). Finally, we used the validation group to validate the model internally; the AUC was 0.987. Calibration curve analysis revealed favorable consistency of nomogram-predicted values compared with real measurements. The C-index was 0.982 (95% CI 0.974-0.999). Conclusion: Logistic regression analysis and machine learning were employed to select four risk factors: Modic changes, sebum thickness, hemoglobin, and glucose. Then, a dynamic prediction model was constructed to help clinicians simplify the monitoring and prevention of SSI.

Identification of spinal tuberculosis subphenotypes using routine clinical data: a study based on unsupervised machine learning.

OBJECTIVE: The identification of spinal tuberculosis subphenotypes is an integral component of precision medicine. However, we lack proper study models to identify subphenotypes in patients with spinal tuberculosis. Here we identified possible subphenotypes of spinal tuberculosis and compared their clinical results. METHODS: A total of 422 patients with spinal tuberculosis who received surgical treatment were enrolled. Clustering analysis was performed using the K-means clustering algorithm and the routinely available clinical data collected from patients within 24 h after admission. Finally, the differences in clinical characteristics, surgical efficacy, and postoperative complications among the subphenotypes were compared. RESULTS: Two subphenotypes of spinal tuberculosis were identified. Laboratory examination results revealed that the levels of more than one inflammatory index in cluster 2 were higher than those in cluster 1. In terms of disease severity, Cluster 2 showed a higher Oswestry Disability Index (ODI), a higher visual analysis scale (VAS) score, and a lower Japanese Orthopedic Association (JOA) score. In addition, in terms of postoperative outcomes, cluster 2 patients were more prone to complications, especially wound infections, and had a longer hospital stay. CONCLUSION: K-means clustering analysis based on conventional available clinical data can rapidly identify two subtypes of spinal tuberculosis with different clinical results. We believe this finding will help clinicians to rapidly and easily identify the subtypes of spinal tuberculosis at the bedside and become the cornerstone of individualized treatment strategies.