OBJECTIVE: To assess the association of microribonucleic acid (miRNA) gene polymorphisms with the risk and clinicopathological characteristics of Crohn's disease (CD) and the influence of miRNA gene variants on the response to ustekinumab (UST) treatment among CD patients. METHODS: From January 2018 to February 2025, 312 patients diagnosed with CD and 527 gender- and age-matched normal controls were selected as the study subjects at the Department of Gastroenterology of the Second Affiliated Hospital of Wenzhou Medical University. Genotypes of miR-155 (rs767649), miR-21 (rs13137), miR-124 (rs531564) and miR-146a (rs57095329, rs2431697) were determined with multiplex polymerase chain reaction-ligase detection reaction (PCR-LDR) technique. The patients were divided into different subgroups according to the Montreal Classification Criteria for CD. Harvey-Bradshaw index (HBI) and simplified endoscopic score for CD were respectively applied to assess the clinical and endoscopic disease activity of CD. Unconditional logistic regression model was employed to analyze the distribution of miRNA gene polymorphisms between the two groups, as well as their influence on the clinicopathological characteristics of CD patients. Among them, 185 CD patients received first-line UST treatment, with the first sufficient dose of UST (6 mg/kg) administered intravenously. Based on the changes in HBI at week 8, the response of patients to UST treatment was evaluated. Unconditional logistic regression model was employed to analyze the distribution of miRNA gene polymorphisms between clinically responsive group (the decline of HBI ≥ 3 scores compared to week 0) and non-responsive group. All of the P values were adjusted by Bonferroni correction. This study has been approved by the Medical Ethics Committee of the Second Affiliated Hospital of Wenzhou Medical University (Ethics No.: 2025-K-12-01). RESULTS: No significant difference was found in the distribution of miRNA gene polymorphisms between the two groups (all P > 0.05). The variant genotype (TC+CC) of rs2431697 was more common among patients with terminal ileal-type and ileocolic-type CD than those with the colonic-type CD (OR = 4.98, 95%CI: 1.49~16.68, P = 0.009, adjusted P = 0.045). However, the opposite conclusion was drawn for the homozygous variant genotype (TT) of rs13137 and variant genotype (GC+CC) of rs531564 (OR = 0.37, 95%CI: 0.18~0.76, P = 0.007, adjusted P = 0.035; OR = 0.36, 95%CI: 0.18~0.73, P = 0.004, adjusted P = 0.020). Compared to patients with non-stricturing and penetrating CD, the variant genotype (AG+GG) and variant allele (G) of rs57095329 were more common in those with stricturing and penetrating CD (OR = 4.06, 95%CI: 2.46~6.71, P < 0.001, adjusted P < 0.005; OR = 3.12, 95%CI: 2.06~4.73, P < 0.001, adjusted P < 0.005). However, the frequencies of variant genotype (AT+TT) and variant allele (T) of rs13137 were lower among patients with stricturing and penetrating CD than in those without (OR = 0.25, 95%CI: 0.15~0.41, P < 0.001, adjusted P < 0.005; OR = 0.45, 95%CI: 0.33~0.63, P < 0.001, adjusted P < 0.005). Additionally, the variant genotype (AG+GG) and variant allele (G) of rs57095329 were more common among those with moderately to severely endoscopic activity than those with mildly endoscopic activity (OR = 2.01, 95%CI: 1.19~3.42, P = 0.009, adjusted P = 0.045; OR = 2.04, 95%CI: 1.28~3.25, P = 0.003, adjusted P = 0.015). In total 117 cases had shown clinical response by week 8, while 68 cases showed no response. Compared with t he clinically non-responsive group, the variant genotype (TC+CC) and variant allele (C) of rs2431697 were more common in the clinically responsive group (OR = 3.86, 95%CI: 1.80~8.32, P = 0.001, adjusted P = 0.005; OR = 2.60, 95%CI: 1.34~5.06, P = 0.005, adjusted P = 0.025). However, the variant genotype (TA+AA) of rs767649 was less frequent in the clinically responsive group than the non-responsive group (OR = 0.40, 95%CI: 0.21~0.74, P = 0.004, adjusted P = 0.020). The same conclusion was drawn for the variant genotype (AT+TT) and variant allele (T) of rs13137 when the clinically responsive group was compared with the non-responsive group (OR = 0.30, 95%CI: 0.14~0.63, P = 0.002, adjusted P = 0.010; OR = 0.54, 95%CI: 0.35~0.82, P = 0.005, adjusted P = 0.025). CONCLUSION Genetic polymorphisms of miRNAs are not associated with the risk of developing CD. The miR-146a (rs57095329) variant may increase the endoscopic activity of CD and the risk for stenosis or penetration. However, the miR-146a (rs2431697) variant may increase the risk of ileal involvement. The miR-21 (rs13137) variant may reduce the risk of ileal involvement and the risk of stenosis or penetration. The miR-124 (rs531564) variant may reduce the risk of ileal involvement. Among patients receiving UST treatment, the miR-146a (rs2431697) variant may increase the clinical response by week 8. However, both the miR-155 (rs767649) and miR-21 (rs13137) variants may decrease the clinical response by week 8.
Humans ; MicroRNAs/genetics* ; Crohn Disease/pathology* ; Male ; Female ; Adult ; Polymorphism, Single Nucleotide ; Middle Aged ; Asian People/genetics* ; Genetic Predisposition to Disease ; Genotype ; Young Adult ; Case-Control Studies ; Adolescent ; East Asian People

Depression is increasingly prevalent among adolescents and can profoundly impact their lives. However, the early detection of depression is often hindered by the time-consuming diagnostic process and the absence of objective biomarkers. In this study, we propose a novel approach for depression detection based on an affective brain-computer interface (aBCI) and the resting-state electroencephalogram (EEG). By fusing EEG features associated with both emotional and resting states, our method captures comprehensive depression-related information. The final depression detection model, derived through decision fusion with multiple independent models, further enhances detection efficacy. Our experiments involved 40 adolescents with depression and 40 matched controls. The proposed model achieved an accuracy of 86.54% on cross-validation and 88.20% on the independent test set, demonstrating the efficiency of multimodal fusion. In addition, further analysis revealed distinct brain activity patterns between the two groups across different modalities. These findings hold promise for new directions in depression detection and intervention.
Humans ; Male ; Female ; Adolescent ; Case-Control Studies ; Depression/diagnosis* ; Early Diagnosis ; Rest ; Electroencephalography/methods* ; Brain-Computer Interfaces ; Models, Psychological ; Reproducibility of Results ; Affect/physiology* ; Photic Stimulation/methods* ; Video Recording ; Brain/physiopathology*

Oral squamous cell carcinoma (OSCC) is the most common manifestation of oral cancer. It has been proposed that periodontal pathogens contribute to OSCC progression, mainly by their virulence factors. However, the main periodontal pathogen and its mechanism to modulate OSCC cells remains not fully understood. In this study we investigate the main host-pathogen pathways in OSCC by computational proteomics and the mechanism behind cancer progression by the oral microbiome. The main host-pathogen pathways were analyzed in the secretome of biopsies from patients with OSCC and healthy controls by mass spectrometry. Then, functional assays were performed to evaluate the host-pathogen pathways highlighted in oral cancer. Host proteins associated with LPS response, cell migration/adhesion, and metabolism of amino acids were significantly upregulated in the human cancer proteome, whereas the complement cascade was downregulated in malignant samples. Then, the microbiome analysis revealed large number and variety of peptides from Fusobacterium nucleatum (F. nucleatum) in OSCC samples, from which several enzymes from the L-glutamate degradation pathway were found, indicating that L-glutamate from cancer cells is used as an energy source, and catabolized into butyrate by the bacteria. In fact, we observed that F. nucleatum modulates the cystine/glutamate antiporter in an OSCC cell line by increasing SLC7A11 expression, promoting L-glutamate efflux and favoring bacterial infection. Finally, our results showed that F. nucleatum and its metabolic derivates promote tumor spheroids growth, spheroids-derived cell detachment, epithelial-mesenchymal transition and Galectin-9 upregulation. Altogether, F. nucleatum promotes pro-tumoral mechanism in oral cancer.
Humans ; Fusobacterium nucleatum/metabolism* ; Mouth Neoplasms/metabolism* ; Disease Progression ; Proteomics ; Carcinoma, Squamous Cell/metabolism* ; Host-Pathogen Interactions ; Metabolic Networks and Pathways ; Case-Control Studies ; Mass Spectrometry

Smoking is a well-established risk factor for periodontitis, yet the precise mechanisms by which smoking contributes to periodontal disease remain poorly understood. Recent advances in spatial transcriptomics have enabled a deeper exploration of the periodontal tissue microenvironment at single-cell resolution, offering new opportunities to investigate these mechanisms. In this study, we utilized Visium HD single-cell spatial transcriptomics to profile gingival tissues from 12 individuals, including those with periodontitis, those with smoking-associated periodontitis, and healthy controls. Our analysis revealed that smoking disrupts the epithelial barrier integrity, induces fibroblast alterations, and dysregulates fibroblast-epithelial cell communication, thereby exacerbating periodontitis. The spatial analysis showed that endothelial cells and macrophages are in close proximity and interact, which further promotes the progression of smoking-induced periodontal disease. Importantly, we found that targeting the endothelial CXCL12 signalling pathway in smoking-associated periodontitis reduced the proinflammatory macrophage phenotype, alleviated epithelial inflammation, and reduced alveolar bone resorption. These findings provide novel insights into the pathogenesis of smoking-associated periodontitis and highlight the potential of targeting the endothelial-macrophage interaction as a therapeutic strategy. Furthermore, this study establishes an essential information resource for investigating the effects of smoking on periodontitis, providing a foundation for future research and therapeutic development for this prevalent and debilitating disease.
Humans ; Gingiva/cytology* ; Smoking/adverse effects* ; Male ; Periodontitis/pathology* ; Single-Cell Analysis ; Female ; Adult ; Middle Aged ; Macrophages ; Fibroblasts ; Endothelial Cells ; Case-Control Studies ; Chemokine CXCL12/metabolism*

Pelvic organ prolapse (POP), whose etiology is influenced by genetic and clinical risk factors, considerably impacts women's quality of life. However, the genetic underpinnings in non-European populations and comprehensive risk models integrating genetic and clinical factors remain underexplored. This study constructed the first polygenic risk score (PRS) for POP in the Chinese population by utilizing 20 disease-associated variants from the largest existing genome-wide association study. We analyzed a discovery cohort of 576 cases and 623 controls and a validation cohort of 264 cases and 200 controls. Results showed that the case group exhibited a significantly higher PRS than the control group. Moreover, the odds ratio of the top 10% risk group was 2.6 times higher than that of the bottom 10%. A high PRS was significantly correlated with POP occurrence in women older than 50 years old and in those with one or no childbirths. As far as we know, the integrated prediction model, which combined PRS and clinical risk factors, demonstrated better predictive accuracy than other existing PRS models. This combined risk assessment model serves as a robust tool for POP risk prediction and stratification, thereby offering insights into individualized preventive measures and treatment strategies in future clinical practice.
Humans ; Female ; Pelvic Organ Prolapse/epidemiology* ; Middle Aged ; Risk Assessment/methods* ; China/epidemiology* ; Multifactorial Inheritance ; Aged ; Risk Factors ; Genome-Wide Association Study ; Genetic Predisposition to Disease ; Case-Control Studies ; Adult ; Polymorphism, Single Nucleotide ; Genetic Risk Score ; East Asian People

OBJECTIVE: To analyze the clinical characteristics of elderly patients with sepsis, identify the key factors affecting their clinical outcomes, construct a death risk assessment scale for elderly patients with sepsis, and evaluate its predictive value. METHODS: A retrospective case-control study was conducted. The clinical data of sepsis patients admitted to intensive care unit (ICU) of the First Affiliated Hospital of Wenzhou Medical University from September 2021 to September 2023 were collected, including basic information, clinical characteristics, and clinical outcomes. The patients were divided into non-elderly group (age ≥ 65 years old) and elderly group (age < 65 years old) based on age. Additionally, the elderly patients were divided into survival group and death group based on their 30-day survival status. The clinical characteristics of elderly patients with sepsis were analyzed. Univariate and multivariate Logistic regression analyses were used to screen the independent risk factors for 30-day death in elderly patients with sepsis, and the regression equation was constructed. The regression equation was simplified, and the death risk assessment scale was established. The predictive value of different scores for the prognosis of elderly patients with sepsis was compared. RESULTS: (1) A total of 833 patients with sepsis were finally enrolled, including 485 in the elderly group and 348 in the non-elderly group. Compared with the non-elderly group, the elderly group showed significantly lower counts of lymphocyte, T cell, CD8⁺ T cell, and the ratio of T cells and CD8⁺ T cells [lymphocyte count (×10⁹/L): 0.71 (0.43, 1.06) vs. 0.83 (0.53, 1.26), T cell count (cells/μL): 394.0 (216.0, 648.0) vs. 490.5 (270.5, 793.0), CD8⁺ T cell count (cells/μL): 126.0 (62.0, 223.5) vs. 180.0 (101.0, 312.0), T cell ratio: 0.60 (0.48, 0.70) vs. 0.64 (0.51, 0.75), CD8⁺ T cell ratio: 0.19 (0.13, 0.28) vs. 0.24 (0.16, 0.34), all P < 0.01], higher natural killer cell (NK cell) count, acute physiology and chronic health evaluation II (APACHE II) score, ratio of invasive mechanical ventilation (IMV) during hospitalization, and 30-day mortality [NK cell count (cells/μL): 112.0 (61.0, 187.5) vs. 95.0 (53.0, 151.0), APACHE II score: 16.00 (12.00, 21.00) vs. 13.00 (8.00, 17.00), IMV ratio: 40.6% (197/485) vs. 31.9% (111/348), 30-day mortality: 28.9% (140/485) vs. 19.5% (68/348), all P < 0.05], and longer length of ICU stay [days: 5.5 (3.0, 10.0) vs. 5.0 (3.0, 8.0), P < 0.05]. There were no statistically significant differences in the levels of inflammatory markers such as C-reactive protein (CRP), procalcitonin (PCT), tumor necrosis factor-α (TNF-α), interferon-γ (IFN-γ), and interleukins (IL-2, IL-4, IL-6, IL-10) between the two groups. (2) In 485 elderly patients with sepsis, 345 survived in 30 days, and 140 died with the 30-day mortality of 28.9%. Compared with the survival group, the patients in the death group were older, and had lower body mass index (BMI), white blood cell count (WBC), PCT, platelet count (PLT) and higher IL-6, IL-10, N-terminal pro-brain natriuretic peptide (NT-proBNP), total bilirubin (TBil), blood lactic acid (Lac), and ratio of in-hospital IMV and continuous renal replacement therapy (CRRT). Multivariate Logistic regression analysis indicated that BMI [odds ratio (OR) = 0.783, 95% confidence interval (95%CI) was 0.678-0.905, P = 0.001], IL-6 (OR = 1.073, 95%CI was 1.004-1.146, P = 0.036), TBil (OR = 1.009, 95%CI was 1.000-1.018, P = 0.045), Lac (OR = 1.211, 95%CI was 1.072-1.367, P = 0.002), and IMV during hospitalization (OR = 6.181, 95%CI was 2.214-17.256, P = 0.001) were independent risk factors for 30-day death in elderly patients with sepsis, and the regression equation was constructed (Logit P = 1.012-0.244×BMI+0.070×IL-6+0.009×TBil+0.190×Lac+1.822×IMV). The regression equation was simplified to construct a death risk assessment scale, namely BITLI score. Receiver operator characteristic curve (ROC curve) analysis showed that the area under the ROC curve (AUC) of BITLI score for predicting death risk was 0.852 (95%CI was 0.769-0.935), and it was higher than APACHE II score (AUC = 0.714, 95%CI was 0.623-0.805) and sequential organ failure assessment (SOFA) score (AUC = 0.685, 95%CI was 0.578-0.793). The determined cut-off value of BITLI score was 1.50, while achieving a sensitivity of 83.3% and specificity of 74.0%. CONCLUSIONS Elderly patients with sepsis often have reduced lymphocyte counts, severe conditions, and poor prognosis. BMI, IL-6, TBil, Lac, and IMV during hospitalization were independent risk factors for 30-day death in elderly patients with sepsis. The BITLI score constructed based above risk factors is more precise and reliable than traditional APACHE II and SOFA scores in predicting the outcomes of elderly patients with sepsis.
Humans ; Sepsis/mortality* ; Aged ; Retrospective Studies ; Risk Assessment ; Case-Control Studies ; Prognosis ; Male ; Female ; Intensive Care Units ; Risk Factors ; Aged, 80 and over ; Logistic Models ; Middle Aged

OBJECTIVE: To observe the characteristics of changes in non-invasive hemodynamic parameters in neonates with septic shock so as to provide clinical reference for diagnosis and treatment. METHODS: A observational study was conducted. The neonates with sepsis complicated with septic shock or not admitted to neonatal intensive care unit (NICU) of the First Affiliated Hospital of Shantou University Medical College were enrolled as the study subjects, who were divided into preterm infant (< 37 weeks) and full-term infant (≥ 37 weeks) according to the gestational age. Healthy full-term infants and hemodynamically stable preterm infants transferring to NICU after birth were enrolled as controls. Electronic cardiometry (EC) was used to measure hemodynamic parameters, including heart rate (HR), mean arterial pressure (MAP), stroke volume (SV), stroke volume index (SVI), cardiac output (CO), cardiac index (CI), systemic vascular resistance (SVR) and systemic vascular resistance index (SVRI), before treatment in the septic shock group, at the time of diagnosis of sepsis in the sepsis without shock group, and before the discharge from the obstetric department or on the day of transferring to NICU in the control group. RESULTS: Finally, 113 neonates with complete data and parental consent for non-invasive hemodynamic monitoring were enrolled, including 32 cases in the septic shock group, 25 cases in the sepsis without shock group and 56 cases in the control group. In the septic shock group, there were 17 cases at the compensated stage and 15 cases at the decompensated stage. There were 21 full-term infants (20 cured or improved and 1 died) and 11 premature infants (7 cured or improved and 4 died), with the mortality of 15.62% (5/32). There were 18 full-term infants and 7 premature infants in the sepsis without shock group and all cured or improved without death. The control group included 28 full-term infants and 28 premature infants transferring to NICU after birth. Non-invasive hemodynamic parameter analysis showed that SV, SVI, CO and CI of full-term infants in the septic shock group were significantly lower than those in the sepsis without shock group and control group [SV (mL): 3.52±0.99 vs. 5.79±1.32, 5.22±1.02, SVI (mL/m²): 16.80 (15.05, 19.65) vs. 27.00 (22.00, 32.00), 27.00 (23.00, 29.75), CO (L/min): 0.52±0.17 vs. 0.80±0.14, 0.72±0.12, CI (mL×s^-1×m^-2): 40.00 (36.67, 49.18) vs. 62.51 (56.34, 70.85), 60.01 (53.34, 69.68), all P < 0.05], while SVR and SVRI were significantly higher than those in the sepsis without shock group and control group [SVR (kPa×s×L^-1): 773.46±291.96 vs. 524.17±84.76, 549.38±72.36, SVRI (kPa×s×L^-1×m^-2): 149.27±51.76 vs. 108.12±12.66, 107.81±11.87, all P < 0.05]. MAP, SV, SVI, CO and CI of preterm infants in the septic shock group were significantly lower than those in the control group [MAP (mmHg, 1 mmHg ≈ 0.133 kPa): 38.55±10.48 vs. 47.46±2.85, SV (mL): 2.45 (1.36, 3.58) vs. 3.96 (3.56, 4.49), SVI (mL/m²): 17.60 (14.20, 25.00) vs. 25.50 (24.00, 29.00), CO (L/min): 0.32 (0.24, 0.63) vs. 0.56 (0.49, 0.63), CI (mL×s^-1×m^-2): 40.01 (33.34, 53.34) vs. 61.68 (56.68, 63.35), all P < 0.05], while SVR and SVRI were similar to the control group [SVR (kPa×s×L^-1): 1 082.88±689.39 vs. 656.63±118.83, SVRI (kPa×s×L^-1×m^-2): 126.00±61.50 vs. 102.37±11.68, both P > 0.05]. Further analysis showed that SV, SVI and CI of neonates at the compensation stage in the septic shock group were significantly lower than those in the control group [SV (mL): 3.60±1.29 vs. 4.73±1.15, SVI (mL/m²): 19.20±8.33 vs. 26.34±3.91, CI (mL×s^-1×m^-2): 46.51±20.34 vs. 61.01±7.67, all P < 0.05], while MAP, SVR and SVRI were significantly higher than those in the control group [MAP (mmHg): 52.06±8.61 vs. 48.54±3.21, SVR (kPa×s×L^-1): 874.95±318.70 vs. 603.01±111.49, SVRI (kPa×s×L^-1×m^-2): 165.07±54.90 vs. 105.09±11.99, all P < 0.05]; MAP, SV, SVI, CO and CI of neonates at the decompensated stage in the septic shock group were significantly lower than those in the control group [MAP (mmHg): 35.13±6.08 vs. 48.54±3.21, SV (mL): 2.89±1.17 vs. 4.73±1.15, SVI (mL/m²): 18.50±4.99 vs. 26.34±3.91, CO (L/min): 0.41±0.19 vs. 0.65±0.15, CI (mL×s^-1×m^-2): 43.34±14.17 vs. 61.01±7.67, all P < 0.05], while SVR and SVRI were similar to the control group [SVR (kPa×s×L^-1): 885.49±628.04 vs. 603.01±111.49, SVRI (kPa×s×L^-1×m^-2): 114.29±43.54 vs. 105.09±11.99, both P > 0.05]. CONCLUSIONS Full-term infant with septic shock exhibit a low cardiac output, high vascular resistance hemodynamic pattern, while preterm infant with septic shock show low cardiac output and normal vascular resistance. At the compensated stage the hemodynamic change is low output and high resistance type, while at the decompensated stage it is low output and normal resistance type. Non-invasive hemodynamic monitoring can assist in the identification of neonatal septic shock and provide basis for clinical diagnosis and treatment.
Humans ; Shock, Septic/physiopathology* ; Infant, Newborn ; Hemodynamics ; Female ; Male ; Case-Control Studies ; Infant, Premature

OBJECTIVE: To analyze the risk factors of hypophosphatemia in patients with acute respiratory distress syndrome (ARDS). METHODS: A retrospective case-control study was conducted. The clinical data of the patients with ARDS admitted to Yanbian University Affiliated Hospital from January 2018 to October 2022 were collected. According to the 1-day serum phosphorus level after intensive care unit (ICU) admission, the patients with normal (0.80-1.45 mmol/L) or elevated (> 1.45 mmol/L) serum phosphorus levels were included in the non-hypophosphatemia group, while those with phosphorus levels lower than 0.80 mmol/L were included in the hypophosphatemia group. The differences in the inflammatory indicators [neutrophils percentage (NEU%), neutrophil count (NEU), lymphocyte count (LYM), high-sensitivity C-reactive protein (hs-CRP)], proteins [total protein (TP), albumin (Alb), prealbumin (PA)], blood lactic acid (Lac), neutrophil/lymphocyte ratio (NLR), neutrophil/albumin ratio (NAR), and blood lactic acid/albumin ratio (L/A) at 1, 2, 4, 6 and 8 days after ICU admission were compared between the two groups. The partial correlation method was used to analyze the correlation between the 1-day serum phosphorus level after ICU admission and the above indicators. Multivariate Logistic regression analysis was adopted to explore the risk factors of hypophosphatemia in patients with ARDS. RESULTS: All 110 patients were enrolled in the final analysis, among which there were 56 cases in the hypophosphatemia group and 54 cases in the non-hypophosphatemia group. At 1 day and 2 days after ICU admission, NEU% in the hypophosphatemia group were significantly higher than those in the non-hypophosphatemia group (1 day: 0.87±0.08 vs. 0.82±0.12, 2 days: 0.87±0.05 vs. 0.83±0.11, both P < 0.05). As the ICU admission time prolonged, LYM in the hypophosphatemia group was basically on the rise, and NEU%, hs-CRP, and NLR were first decreased and then increased. At 1 day after ICU admission, TP, Alb and PA in the hypophosphatemia group were significantly lower than those in the non-hypophosphatemia group [TP (g/L): 52.96±8.42 vs. 56.47±8.36, Alb (g/L): 29.73±5.83 vs. 33.08±7.35, PA (g/L): 69.95±50.72 vs. 121.50±82.42, all P < 0.05]. As the ICU admission time prolonged, TP and Alb in the hypophosphatemia group were basically showed a trend of first decreasing and then increasing, but at 8 days, Alb was still lower than that at 1 day, and PA basically showed an upward trend. In the non-hypophosphatemia group, the change trends of TP and Alb were consistent with those in the hypophosphatemia group. Lac and L/A both showed a downward trend in the two groups. Partial correlation analysis showed that 1-day serum phosphorus level after ICU admission was significantly negatively correlated with NEU% and hs-CRP (r value was -0.229 and -0.286, respectively, both P < 0.05), and significantly positively correlated with LYM and PA (r value was 0.231 and 0.311, respectively, both P < 0.05). Multivariate Logistic regression analysis showed that 1-day NEU% [odds ratio (OR) = 0.932, 95% confidence interval (95%CI) was 0.873-0.996, P = 0.038] and Alb (OR = 1.167, 95%CI was 1.040-1.308, P = 0.008) were the independent risk factors for hypophosphatemia in ARDS patients. CONCLUSION NEU% and Alb at 1 day after ICU admission are independent risk factors for hypophosphatemia in patients with ARDS.
Humans ; Hypophosphatemia/etiology* ; Respiratory Distress Syndrome/blood* ; Risk Factors ; Retrospective Studies ; Case-Control Studies ; Intensive Care Units ; Male ; Female ; Phosphorus/blood* ; Middle Aged ; Neutrophils ; Aged ; C-Reactive Protein

OBJECTIVE: To identify early potential risk factors for hospital-acquired pneumonia (HAP) in patients with mild traumatic brain injury (mTBI), construct a risk prediction model, and evaluate its predictive efficacy. METHODS: A case-control study was conducted using clinical data from mTBI patients admitted to the neurosurgery department of Changzhou Second People's Hospital from September 2021 to September 2023. The patients were divided into two groups based on whether they developed HAP. Clinical data within 48 hours of admission were statistically analyzed to identify factors influencing HAP occurrence through univariate analysis. Least absolute shrinkage and selection operator (LASSO) regression analysis was employed for feature selection to identify the most influential variables. The dataset was divided into training and validation sets in a 7:3 ratio. A multivariate Logistic regression analysis was then performed using the training set to construct the prediction model, exploring the risk factors for HAP in mTBI patients and conducting internal validation in the validation set. Receiver operator characteristic curve (ROC curve), decision curve analysis (DCA), and calibration curve were utilized to assess the sensitivity, specificity, decision value, and predictive accuracy of the prediction model. RESULTS: A total of 677 mTBI patients were included, with 257 in the HAP group and 420 in the non-HAP group. The significant differences were found between the two groups in terms of age, maximum body temperature (MaxT), maximum heart rate (MaxHR), maximum systolic blood pressure (MaxSBP), minimum systolic blood pressure (MinSBP), maximum respiratory rate (MaxRR), cause of injury, and laboratory indicators [C-reactive protein (CRP), procalcitonin (PCT), neutrophil count (NEUT), erythrocyte sedimentation rate (ESR), fibrinogen (FBG), fibrinogen equivalent units (FEU), prothrombin time (PT), activated partial thromboplastin time (APTT), total cholesterol (TC), lactate dehydrogenase (LDH), prealbumin (PAB), albumin (Alb), blood urea nitrogen (BUN), serum creatinine (SCr), hematocrit (HCT), hemoglobin (Hb), platelet count (PLT), glucose (Glu), K⁺, Na⁺], suggesting they could be potential risk factors for HAP in mTBI patients. After LASSO regression analysis, the key risk factors were enrolled in the multivariate Logistic regression analysis. The results revealed that the cause of injury being a traffic accident [odds ratio (OR) = 2.199, 95% confidence interval (95%CI) was 1.124-4.398, P = 0.023], NEUT (OR = 1.330, 95%CI was 1.214-1.469, P < 0.001), ESR (OR = 1.053, 95%CI was 1.019-1.090, P = 0.003), FBG (OR = 0.272, 95%CI was 0.158-0.445, P < 0.001), PT (OR = 0.253, 95%CI was 0.144-0.422, P < 0.001), APTT (OR = 0.689, 95%CI was 0.578-0.811, P < 0.001), Alb (OR = 0.734, 95%CI was 0.654-0.815, P < 0.001), BUN (OR = 0.720, 95%CI was 0.547-0.934, P = 0.016), and Na⁺ (OR = 0.756, 95%CI was 0.670-0.843, P < 0.001) could serve as main risk factors for constructing the prediction model. Calibration curves demonstrated good calibration of the prediction model in both training and validation sets with no evident over fitting. ROC curve analysis showed that the area under the ROC curve (AUC) of the prediction model in the training set was 0.943 (95%CI was 0.921-0.965, P < 0.001), with a sensitivity of 83.6% and a specificity of 91.5%. In the validation set, the AUC was 0.917 (95%CI was 0.878-0.957, P < 0.001), with a sensitivity of 90.1% and a specificity of 85.0%. DCA indicated that the prediction model had a high net benefit, suggesting practical clinical applicability. CONCLUSIONS The cause of injury being a traffic accident, NEUT, ESR, FBG, PT, APTT, Alb, BUN, and Na⁺ are identified as major risk factors influencing the occurrence of HAP in mTBI patients. The prediction model constructed using these parameters effectively assesses the likelihood of HAP in mTBI patients.
Humans ; Risk Factors ; Case-Control Studies ; Logistic Models ; Healthcare-Associated Pneumonia/epidemiology* ; Brain Injuries, Traumatic/complications* ; Male ; Female ; ROC Curve ; Pneumonia/etiology* ; Middle Aged ; Adult

OBJECTIVE: To analyze the factors influencing the development of refeeding syndrome (RFS) in patients with sepsis and its impact on clinical prognosis. METHODS: A retrospective case-control study method was used to collect the clinical data of patients with sepsis admitted to the intensive care unit (ICU) of Qingdao Municipal Hospital from December 2018 to December 2023. The patients were divided into RFS and non-RFS groups according to whether RFS occurred, and the basic data, nutritional status and assessment scale, laboratory indicators, nutritional intake, medical history and prognosis were compared between the two groups. Binary multifactorial Logistic regression analysis was used to screen the influencing factors of the occurrence of RFS in patients with sepsis. RESULTS: A total of 544 patients with sepsis were finally enrolled, of whom 250 did not develop RFS and 294 developed RFS, with an incidence of 54.0%. Compared with the non-RFS group, the patients in the RFS group had lower body mass index (BMI), albumin, prealbumin, baseline electrolytes (serum phosphorus, serum potassium, and serum magnesium), creatinine-height index, and protein intake, and had higher nutritional risk screening 2002 (NRS2002) score, sequential organ failure assessment (SOFA) score, calorie intake, and the proportions of feedings during the 48 hours of ICU admission, history of diabetes and septic shock. Binary multifactorial Logistic regression analysis showed that BMI [odds ratio (OR) = 0.910, 95% confidence interval (95%CI) was 0.857-0.947, P < 0.001], SOFA score (OR = 1.166, 95%CI was 1.085-1.254, P < 0.001), albumin (OR = 0.946, 95%CI was 0.902-0.991, P = 0.019), baseline serum phosphorus (OR = 0.343, 95%CI was 0.171-0.689, P = 0.003), baseline serum potassium (OR = 0.531, 95%CI was 0.377-0.746, P < 0.001), creatinine-height index (OR = 0.891, 95%CI was 0.819-0.970, P = 0.008), caloric intake (OR = 1.108, 95%CI was 1.043-1.178, P = 0.001), protein intake (OR = 0.107, 95%CI was 0.044-0.260, P < 0.001), and feedings during the 48 hours of ICU admission (OR = 0.592, 95%CI was 0.359-0.977, P = 0.040) and septic shock (OR = 0.538, 95%CI was 0.300-0.963, P = 0.037) were independent influence factors on the occurrence of RFS in septic patients. Of the 544 patients, 267 died at 28 days, with a mortality of 49.1%. The 28-day mortality of patients in the RFS group was significantly higher than that in the non-RFS group [54.4% (160/294) vs. 42.8% (107/250); χ² = 7.302, P = 0.007]. 544 patients had a length of ICU stay of 20 (17, 24) days. The patients in the RFS group had a significantly longer length of ICU stay than that in the non-RFS group [days: 20 (17, 25) vs. 19 (17, 23); Z = -2.312, P = 0.021]. CONCLUSIONS The incidence of RFS in septic patients is high. Factors influencing the occurrence of RFS in septic patients include BMI, SOFA score, albumin, baseline serum phosphorus, baseline serum potassium, caloric intake, protein intake, feeding within 48 hours of ICU admission, and septic shock. RFS prolongs the length of ICU stay and increases the 28-day mortality in patients with sepsis.
Humans ; Retrospective Studies ; Sepsis/complications* ; Prognosis ; Refeeding Syndrome/etiology* ; Case-Control Studies ; Intensive Care Units ; Male ; Nutritional Status ; Female ; Risk Factors ; Middle Aged ; Logistic Models ; Body Mass Index ; Aged