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.

Artificial intelligence in predicting recurrence after first-line treatment of liver cancer: a systematic review and meta-analysis.

BACKGROUND: The aim of this study was to conduct a systematic review and meta-analysis to comprehensively evaluate the performance and methodological quality of artificial intelligence (AI) in predicting recurrence after single first-line treatment for liver cancer. METHODS: A rigorous and systematic evaluation was conducted on the AI studies related to recurrence after single first-line treatment for liver cancer, retrieved from the PubMed, Embase, Web of Science, Cochrane Library, and CNKI databases. The area under the curve (AUC), sensitivity (SENC), and specificity (SPEC) of each study were extracted for meta-analysis. RESULTS: Six percutaneous ablation (PA) studies, 16 surgical resection (SR) studies, and 5 transarterial chemoembolization (TACE) studies were included in the meta-analysis for predicting recurrence after hepatocellular carcinoma (HCC) treatment, respectively. Four SR studies and 2 PA studies were included in the meta-analysis for recurrence after intrahepatic cholangiocarcinoma (ICC) and colorectal cancer liver metastasis (CRLM) treatment. The pooled SENC, SEPC, and AUC of AI in predicting recurrence after primary HCC treatment via PA, SR, and TACE were 0.78, 0.90, and 0.92; 0.81, 0.77, and 0.86; and 0.73, 0.79, and 0.79, respectively. The values for ICC treated with SR and CRLM treated with PA were 0.85, 0.71, 0.86 and 0.69, 0.63,0.74, respectively. CONCLUSION: This systematic review and meta-analysis demonstrates the comprehensive application value of AI in predicting recurrence after a single first-line treatment of liver cancer, with satisfactory results, indicating the clinical translation potential of AI in predicting recurrence after liver cancer treatment.

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.

Base-Mediated Regioselective [3 + 3] Annulation of Alkylidene Malononitriles with Trifluoromethyl Alkenes via Dual C-F Bond Cleavage.

A base-mediated regioselective [3 + 3] annulation of alkylidene malononitriles with trifluoromethyl alkenes was described. The reaction proceeds through sequential intermolecular SN2' and intramolecular SNV-type cyclization by cleaving dual C-F bonds in a trifluoromethyl group, which discriminate multiple carbon-nucleophilic sites using a single base. Various bicycles bearing a monofluorocyclohexene motif were assembled from readily available starting materials under mild conditions via a one-pot cascade approach.

Identification of BiP as a temperature sensor mediating temperature-induced germline sex reversal in C. elegans.

Sex determination in animals is not only determined by karyotype but can also be modulated by environmental cues like temperature via unclear transduction mechanisms. Moreover, in contrast to earlier views that sex may exclusively be determined by either karyotype or temperature, recent observations suggest that these factors rather co-regulate sex, posing another mechanistic mystery. Here, we discovered that certain wild-isolated and mutant C. elegans strains displayed genotypic germline sex determination (GGSD), but with a temperature-override mechanism. Further, we found that BiP, an ER chaperone, transduces temperature information into a germline sex-governing signal, thereby enabling the coexistence of GGSD and temperature-dependent germline sex determination (TGSD). At the molecular level, increased ER protein-folding requirements upon increased temperatures lead to BiP sequestration, resulting in ERAD-dependent degradation of the oocyte fate-driving factor, TRA-2, thus promoting male germline fate. Remarkably, experimentally manipulating BiP or TRA-2 expression allows to switch between GGSD and TGSD. Physiologically, TGSD allows C. elegans hermaphrodites to maintain brood size at warmer temperatures. Moreover, BiP can also influence germline sex determination in a different, non-hermaphroditic nematode species. Collectively, our findings identify thermosensitive BiP as a conserved temperature sensor in TGSD, and provide mechanistic insights into the transition between GGSD and TGSD.

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.

Ultrasound-based deep learning radiomics nomogram for differentiating mass mastitis from invasive breast cancer.

BACKGROUND: The purpose of this study is to develop and validate the potential value of the deep learning radiomics nomogram (DLRN) based on ultrasound to differentiate mass mastitis (MM) and invasive breast cancer (IBC). METHODS: 50 cases of MM and 180 cases of IBC with ultrasound Breast Imaging Reporting and Data System 4 category were recruited (training cohort, n = 161, validation cohort, n = 69). Based on PyRadiomics and ResNet50 extractors, radiomics and deep learning features were extracted, respectively. Based on supervised machine learning methods such as logistic regression, random forest, and support vector machine, as well as unsupervised machine learning methods using K-means clustering analysis, the differences in features between MM and IBC were analyzed to develop DLRN. The performance of DLRN had been evaluated by receiver operating characteristic curve, calibration, and clinical practicality. RESULTS: Supervised machine learning results showed that compared with radiomics models, especially random forest models, deep learning models were better at recognizing MM and IBC. The area under the curve (AUC) of the validation cohort was 0.84, the accuracy was 0.83, the sensitivity was 0.73, and the specificity was 0.83. Compared to radiomics or deep learning models, DLRN even further improved discrimination ability (AUC of 0.90 and 0.90, accuracy of 0.83 and 0.88 for training and validation cohorts), which had better clinical benefits and good calibratability. In addition, the information heterogeneity of deep learning features in MM and IBC was validated again through unsupervised machine learning clustering analysis, indicating that MM had a unique features phenotype. CONCLUSION: The DLRN developed based on radiomics and deep learning features of ultrasound images has potential clinical value in effectively distinguishing between MM and IBC. DLRN breaks through visual limitations and quantifies more image information related to MM based on computers, further utilizing machine learning to effectively utilize this information for clinical decision-making. As DLRN becomes an autonomous screening system, it will improve the recognition rate of MM in grassroots hospitals and reduce the possibility of incorrect treatment and overtreatment.

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.

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 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.

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.

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.