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 investigate the predictive value of early lactate/albumin ratio (LAR) combined with quick sequential organ failure assessment (qSOFA) for the 28-day prognosis of patients with sepsis caused by emergency community-acquired pneumonia (CAP). METHODS: The clinical data of patients with sepsis caused by CAP admitted to the department of emergency of Beijing Haidian Hospital from June 2021 to August 2022 were retrospectively analyzed, including gender, age, comorbidities, lactic acid (Lac), serum albumin (Alb), LAR, procalcitonin (PCT) within 1 hour, and 28-day prognosis. Patients were divided into two groups based on 28-day prognosis, and risk factors affecting patients' prognosis were analyzed using univariate and multivariate Cox regression methods. Patients were divided into two groups according to the best cut-off value of LAR, and Kaplan-Meier survival curves were used to analyze the 28-day cumulative survival of patients in each group. Time-dependent receiver operator characteristic curve (ROC curve) were plotted to analyze the predictive value of sequential organ failure assessment (SOFA), acute physiology and chronic health evaluation II (APACHE II), and qSOFA+LAR score on the prognosis of patients with sepsis caused by CAP at 28 days. The area under the curve (AUC) was calculated and compared. RESULTS: A total of 116 patients with sepsis caused by CAP were included, of whom 80 survived at 28 days and 36 died, 28-day mortality of 31.0%. There were no statistically significant differences in age, gender, comorbidities, pH, platelet count, and fibrinogen between the survival and death groups, and there were significantly differences in blood urea nitrogen (BUN), white blood cell count (WBC), hemoglobin, Lac, Alb, PCT, D-dimer, LAR, as well as qSOFA score, SOFA score, and APACHE II score. Univariate Cox regression analyses showed that BUN, WBC, pH, Lac, Alb, PCT, LAR, qSOFA score, SOFA score, and APACHE II score were associated with mortality outcome. Multifactorial Cox regression analysis of the above variables showed that BUN, WBC, PCT, and APACHE II score were independent risk factors for 28-day death in the emergency department in patients with sepsis caused by CAP [hazard ratio (HR) were 1.081, 0.892, 1.034, and 1.135, respectively, all P < 0.05]. The best cut-off value of early LAR for predicting the 28-day prognosis of sepsis patients was 0.088, the Kaplan-Meier survival curve showed that the 28-day cumulative survival rate of sepsis patients in the LAR ≤ 0.088 group was significantly higher than that in the LAR > 0.088 group [82.9% (63/76) vs. 42.5% (17/40), Log-Rank test: χ² = 22.51, P < 0.001]. The qSOFA+LAR score was calculated based on the LAR cut-off value and qSOFA score, and ROC curve analysis showed that the AUCs of SOFA score, APACHE II score, and qSOFA+LAR score for predicting 28-day death of patients with sepsis caued by CAP were 0.741, 0.774, and 0.709, respectively, with the AUC of qSOFA+LAR score slightly lower than those of SOFA score and APACHE II score, but there were no significantly differences. When the best cut-off value of qSOFA+LAR score was 1, the sensitivity was 63.9% and the specificity was 80.0%. CONCLUSION The qSOFA+LAR score has predictive value for the 28-day prognosis of patients with sepsis caused by CAP in the emergency department, its predictive value is comparable to the SOFA score and the APACHE II score, and it is more convenient for early use in the emergency department.
Emergency Service, Hospital/statistics & numerical data* ; Sepsis/etiology* ; Prognosis ; Community-Acquired Pneumonia/mortality* ; Organ Dysfunction Scores ; Predictive Value of Tests ; Lactic Acid/blood* ; Serum Albumin, Human/analysis* ; Biomarkers/blood* ; Retrospective Studies ; Hospital Mortality ; Kaplan-Meier Estimate ; APACHE ; Procalcitonin/blood* ; ROC Curve ; Area Under Curve ; Humans

OBJECTIVE: To explore the feasibility of incorporating simple bedside indicators into death predictive model for elderly critically ill patients based on interpretability machine learning algorithms, providing a new scheme for clinical disease assessment. METHODS: Elderly critically ill patients aged ≥ 65 years who were hospitalized in the intensive care unit (ICU) of Tacheng People's Hospital of Ili Kazak Autonomous Prefecture from June 2017 to May 2020 were retrospectively selected. Basic parameters including demographic characteristics, basic vital signs and fluid intake and output within 24 hours after admission, as well acute physiology and chronic health evaluation II (APACHE II), Glasgow coma score (GCS) and sequential organ failure assessment (SOFA) were also collected. According to outcomes in hospital, patients were divided into survival group and death group. Four datasets were constructed respectively, namely baseline dataset (B), including age, body temperature, heart rate, pulse oxygen saturation, respiratory rate, mean arterial pressure, urine output volume, infusion volume, and crystal solution volume; B+APACHE II dataset (BA), B+GCS dataset (BG), and B+SOFA dataset (BS). Then three machine learning algorithms, Logistic regression (LR), extreme gradient boosting (XGboost) and gradient boosting decision tree (GBDT) were used to develop the corresponding mortality predictive models within four datasets. The feature importance histogram of each prediction model was drawn by SHapley additive explanation (SHAP) method. The area under curve (AUC), accuracy and F1 score of each model were compared to determine the optimal prediction model and then illuminate the nomogram. RESULTS: A total of 392 patients were collected, including 341 in the survival group and 51 in the death group. There were statistically significant differences in heart rate, pulse oxygen saturation, mean arterial pressure, infusion volume, crystal solution volume, and etiological distribution between the two groups. The top three causes of death were shock, cerebral hemorrhage, and chronic obstructive pulmonary disease. Among the 12 prognostic models trained by three machine learning algorithms, overall performance of prognostic models based on B dataset was behind, whereas the LR model trained by BA dataset achieved the best performance than others with AUC of 0.767 [95% confidence interval (95%CI) was 0.692-0.836], accuracy of 0.875 (95%CI was 0.837-0.903) and F1 score of 0.190. The top 3 variables in this model were crystal solution volume with first 24 hours, heart rate and mean arterial pressure. The nomogram of the model showed that the total score between 150 and 230 were advisable. CONCLUSION The interpretable machine learning model including simple bedside parameters combined with APACHE II score could effectively identify the risk of death in elderly patients with critically illness.
Humans ; Critical Illness ; Machine Learning ; Aged ; Algorithms ; Intensive Care Units ; Retrospective Studies ; APACHE ; Prognosis ; Organ Dysfunction Scores ; Hospital Mortality ; Male ; Female

Sepsis is a life-threatening organ dysfunction syndrome caused by a dysregulated host response to infection. Due to different infection sources, pathogens and basic conditions of patients, there is significant heterogeneity in clinical manifestations, response to treatment and prognosis of patients with sepsis. Accurate classification and individualized treatment of sepsis will help to further improve the prognosis of patients with sepsis. In recent years, the integration of artificial intelligence and bioinformatics has brought new opportunities for the research of sepsis classification. This review systematically introduces a variety of sepsis classification methods and their clinical application value. The clinical data in the electronic medical record, such as the dynamic changes of vital signs such as body temperature, can be used as the basis for sepsis classification. Different subtypes of body temperature trajectories have differences in physiological characteristics and prognosis, which contributes to predict the prognosis of patients and guide fluid management strategies. Biomarker classification can more comprehensively reflect the pathophysiological state of patients. Immune index classification is helpful to identify immunocompromised patients so as to carry out targeted immunotherapy. Transcriptome data and genotyping reveal the heterogeneity of sepsis at the molecular level and provide a new perspective for precision medicine. In addition, a detailed systematic review of sepsis-related organ function damage, such as acute respiratory distress syndrome (ARDS), acute kidney injury (AKI), and acute liver injury, has also been conducted, which is helpful to develop targeted organ protection and treatment strategies. These typing methods have shown good application prospects in clinical practice. However, there are still limitations in the current research, such as typing stability and biomarker selection, which need to be further explored. Future research should focus on the development of stable and efficient typing tools to achieve precise treatment of sepsis and improve the prognosis of patients.
Humans ; Sepsis/classification* ; Multiple Organ Failure/classification* ; Prognosis ; Artificial Intelligence ; Biomarkers ; Computational Biology ; Respiratory Distress Syndrome

OBJECTIVE: To develop a method for identifying high-risk patients among septic populations requiring mechanical ventilation, and to conduct phenotypic analysis based on this method. METHODS: Data from four sources were utilized: the Medical Information Mart for Intensive Care (MIMIC-IV 2.0, MIMIC-III 1.4), the Philips eICU-Collaborative Research Database 2.0 (eICU-CRD 2.0), and the Anhui Medical University Second Affiliated Hospital dataset. The adult patients in intensive care unit (ICU) who met Sepsis-3 and received invasive mechanical ventilation (IMV) on the first day of first admission were enrolled. The MIMIC-IV dataset with the highest data integrity was divided into a training set and a test set at a 6:1 ratio, while the remaining datasets were served as validation sets. The demographic information, comorbidities, laboratory indicators, commonly used ICU scores, and treatment measures of patients were extracted. Clinical data collected within first day of ICU admission were used to calculate the sequential organ failure assessment (SOFA) score. K-means clustering was applied to cluster SOFA score components, and the sum of squared errors (SSE) and Davies-Bouldin index (DBI) were used to determine the optimal number of disease subtypes. For clustering results, normalized methods were employed to compare baseline characteristics by visualization, and Kaplan-Meier curves were used to analyze clinical outcomes across phenotypes. RESULTS: This study enrolled patients from MIMIC-IV dataset (n = 11 166), MIMIC-III dataset (n = 4 821), eICU-CRD dataset (n = 6 624), and a local dataset (n = 110), with the four datasets showing similar median ages and male proportions exceeding 50%; using 85% of the MIMIC-IV dataset as the training set, 15% as the test set, and the rest dataset as the validation set. K-means clustering based on the six-item SOFA score was performed to determine the optimal number of clusters as 3, and patients were finally classified into three phenotypes. In the training set, compared with the patients with phenotype II and phenotype III, those with phenotype I had the more severe circulatory and respiratory dysfunction, a higher proportion of vasoactive drug usage, more obvious metabolic acidosis and hypoxia, and a higher incidence of congestive heart failure. The patients with phenotype II was dominated by respiratory dysfunction with higher visceral injury. The patients with phenotype III had relatively stable organ function. The above characteristics were consistent in both the test and validation sets. Analysis of infection-related indicators showed that the patients with phenotype I had the highest SOFA score within 7 days after ICU admission, initial decreases and later increases in platelet count (PLT), and higher counts of neutrophils, lymphocytes, and monocytes as compared with those with phenotype II and phenotype III, their blood cultures had a higher positivity rates for Gram-positive bacteria, Gram-negative bacteria and fungi as compared with those with phenotype II and phenotype III. The Kaplan-Meier curve indicated that in the training, test, and validation sets, the 28-day cumulative mortality of patients with phenotype I was significantly higher than that of patients with phenotypes II and phenotype III. CONCLUSIONS Three distinct phenotypes in septic patients receiving IMV based on unsupervised machine learning is derived, among which phenotype I, characterized by cardiorespiratory failure, can be used for the early identification of high-risk patients in this population. Moreover, this population is more prone to bloodstream infections, posing a high risk and having a poor prognosis.
Humans ; Machine Learning ; Sepsis/therapy* ; Respiration, Artificial ; Intensive Care Units ; Organ Dysfunction Scores ; Male ; Female ; Middle Aged ; Adult

OBJECTIVE: To review the nomenclature, functional unit construction, technical essentials, and prevention and treatment of complications of functional perforator flaps, so as to provide references for the structural and functional reconstruction of composite tissue defects. METHODS: By retrieving and analyzing domestic and foreign literature on anatomical research, technical innovation and clinical application of functional design and application of perforator flaps, combined with the clinical practice of our team, the methods for harvesting and integrating functional units of perforator flaps were summarized. RESULTS: Functional perforator flap refers to a perforator flap that, on the basis of perforator blood supply, carries one or more tissue functional units (such as muscles, nerves, lymphatic vessels, lymph nodes, bones, mucous membranes, joints or articular cartilages, etc.) with sufficient blood supply located in the supra-fascia and/or sub-fascia, and is used to reconstruct one or more functions of the recipient site. The design and transfer of functional perforator flaps should not only meet the needs of precise coverage of the wound, but also reconstruct the functions of the recipient site such as muscle contraction, flap sensation, lymphatic drainage, blood flow bridging, bone growth, glandular secretion or joint movement, while avoiding iatrogenic dysfunction in the donor site. CONCLUSION Functional perforator flaps have broken through the limitation of "wound coverage" and realize the integrated reconstruction of "structure-function-aesthetics".
Humans ; Perforator Flap/blood supply* ; Plastic Surgery Procedures/methods* ; Soft Tissue Injuries/surgery* ; Tissue and Organ Harvesting/methods* ; Skin Transplantation/methods*

OBJECTIVE: To explore the application value of infrared thermography in the design and harvesting of ultrathin anterolateral thigh perforator flaps. METHODS: Between June 2024 and December 2024, 9 cases of ultrathin anterolateral thigh perforator flaps were designed and harvested with the assistance of infrared thermography. There were 7 males and 2 females, aged 21-61 years (mean, 39.8 years). The body mass index ranged from 19.49 to 26.45 kg/m² (mean, 23.85 kg/m²). Causes of injury included 5 cases of traffic accident injuries and 4 cases of machine crush injuries. There were 3 cases of leg wounds, 2 cases of foot wounds, and 4 cases of hand wounds. After debridement, the size of wound ranged from 7 cm×4 cm to 13 cm×11 cm. The time from admission to flap repair surgery was 5-12 days (mean, 7 days). Preoperatively, perforator localization was performed using a traditional Doppler flow detector and infrared thermography, respectively. The results were compared with the actual intraoperative locations; a discrepancy ≤10 mm was considered as consistent localization (positive), and the positive predictive value was calculated. All 9 cases were repaired with ultrathin anterolateral thigh perforator flaps designed and harvested based on thermographic images. The size of flap ranged from 8 cm×5 cm to 14 cm×8 cm, with a thickness of 3-6 mm (mean, 5.2 mm). One donor site was repaired with a full-thickness skin graft, and the others were sutured directly. Postoperatively, anti-inflammatory, anticoagulant, and anti-vascular spasm treatments were administered, and follow-up was conducted. RESULTS: The Doppler flow detector identified 22 perforating vessels within the set range, among which 16 were confirmed as superficial fascia layer perforators intraoperatively, with a positive predictive value of 72.7%. The infrared thermograph detected 23 superficial fascia layer perforating vessels, and 21 were verified intraoperatively, with a positive predictive value of 91.3%. There was no significant difference between the two methods [OR (95%CI)=3.93 (0.70, 22.15), P=0.100]. The perforator localization time of the infrared thermograph was (5.1±1.3) minutes, which was significantly shorter than that of the Doppler flow detector [(10.1±2.6) minutes; MD (95%CI)=-5.00 (-7.08, -2.91), P<0.001]. Postoperatively, 1 case of distal flap necrosis healed after dressing change; all other flaps survived successfully. The skin grafts at donor site survived, and all incisions healed by first intention. All patients were followed up 3-6 months (mean, 4.7 months). No pain or other discomfort occurred at the donor or recipient sites. All patients with foot wounds could walk with shoes, and no secondary flap revision was required. Flaps in 3 hand wound cases, 2 foot wound cases, and 3 leg wound cases recovered light touch and pressure sensation, but not pain or temperature sensation; the remaining 2 cases had no sensory recovery. CONCLUSION Preoperative localization using infrared thermography for repairing ultrathin anterolateral thigh perforator flaps can help evaluate the blood supply status of perforators, reduce complications, and improve surgical safety and flap survival rate.
Humans ; Perforator Flap/blood supply* ; Adult ; Male ; Thermography/methods* ; Female ; Thigh/blood supply* ; Middle Aged ; Plastic Surgery Procedures/methods* ; Tissue and Organ Harvesting/methods* ; Infrared Rays ; Skin Transplantation/methods* ; Soft Tissue Injuries/surgery* ; Young Adult

BACKGROUND: Fibrosis of the connective tissue in the vaginal wall predominates in pelvic organ prolapse (POP), which is characterized by excessive fibroblast-to-myofibroblast differentiation and abnormal deposition of the extracellular matrix (ECM). Our study aimed to investigate the effect of ECM stiffness on vaginal fibroblasts and to explore the role of methyltransferase 3 (METTL3) in the development of POP. METHODS: Polyacrylamide hydrogels were applied to create an ECM microenvironment with variable stiffness to evaluate the effects of ECM stiffness on the proliferation, differentiation, and expression of ECM components in vaginal fibroblasts. METTL3 small interfering RNA and an overexpression vector were transfected into vaginal fibroblasts to evaluate the effects of METTL3 silencing and overexpression on matrix stiffness-induced vaginal fibroblast-to-myofibroblast differentiation and abnormal modulation of the ECM. Both procedures were detected by 5-ethynyl-2'-deoxyuridine (EdU) staining, Western blotting (WB), quantitative real-time polymerase chain reaction (RT-qPCR), and immunofluorescence (IF). RESULTS: Vaginal fibroblasts from POP patients exhibited increased proliferation ability, increased expression of α-smooth muscle actin (α-SMA), decreased expression of collagen I/III, and significantly decreased expression of tissue inhibitors of matrix metalloproteinases (TIMPs) in the stiff matrix ( P <0.05). Compared with those from non-POP patients, vaginal wall tissues from POP patients demonstrated a significant increase in METTL3 content ( P <0.05). However, silencing METTL3 expression in vaginal fibroblasts with high ECM stiffness resulted in decreased proliferation ability, decreased α-SMA expression, an increased ratio of collagen I/III, and increased TIMP1 and TIMP2 expression. Conversely, METTL3 overexpression significantly promoted the process of increased proliferation ability, increased α-SMA expression, decreased ratio of collagen I/III and decreased TIMP1 and TIMP2 expression in the soft matrix ( P <0.05). CONCLUSIONS Elevated ECM stiffness can promote excessive proliferation, differentiation, and abnormal ECM modulation, and the expression of METTL3 plays an important role in alleviating or aggravating matrix stiffness-induced vaginal fibroblast-to-myofibroblast differentiation and abnormal ECM modulation.
Humans ; Female ; Extracellular Matrix/metabolism* ; Cell Differentiation/genetics* ; Methyltransferases/metabolism* ; Pelvic Organ Prolapse/pathology* ; Fibroblasts/metabolism* ; Myofibroblasts/metabolism* ; Vagina/metabolism* ; Cell Proliferation/physiology* ; Cells, Cultured ; Middle Aged

In recent years, digital bone harvesting and grafting technology in dental implantology has emerged as a cutting-edge advancement in the field of oral medicine, gaining widespread application in the treatment of complex bone defect cases. By integrating digital imaging, virtual design, and precise surgical guidance, this technology has significantly enhanced the success rate of dental implants and improved patient outcomes. However, the rapid development of this technology has also highlighted the lack of standardized clinical protocols, necessitating the establishment of unified guidelines through expert consensus. This article provides a detailed overview of the development process of the group standard Clinical Protocol for Bone Harvesting and Grafting under Digital Guidance in Oral Implantology and offers an in-depth interpretation of its key components, aiming to serve as a valuable reference for clinical practice and academic research.
Humans ; Bone Transplantation/methods* ; Surgery, Computer-Assisted/methods* ; Dental Implantation/methods* ; Tissue and Organ Harvesting/methods* ; Clinical Protocols

Human herpes virus 6 (HHV-6) infection generally occurs in infancy,and the virus is mostly latent in monocytes and macrophages in the peripheral blood.HHV-6 is reactivated when the immune function is suppressed.HHV-6 DNA can be detected in peripheral blood mononuclear cells in more than 80% of healthy adults in China,while the incidence is low in the adults with normal immune functions.This paper reports a case of multiple organ dysfunction syndrome caused by HHV-6B infection in an adult with normal immune functions.High-throughput sequencing revealed the presence of HHV-6B with high confidence in blood and cerebrospinal fluid.
Adult ; Humans ; Herpesvirus 6, Human ; Multiple Organ Failure/etiology* ; Roseolovirus Infections/complications*