Acute compartment syndrome (ACS) is common in trauma, challenging early diagnosis and treatment. An accurate ACS model is vital for its management. Most prior animal models have induced intrafascial hypertension by exogenous interventions, focusing on the study of secondary injury post-ACS formation. A novel rat model with spontaneous ACS formation after trauma was constructed, emphasizing early onset and development. The 300-mmHg pressure simulated by a blood pressure cuff was applied to rat hindlimbs for 3 h. A balloon catheter connected to a manometer was developed, and intracompartmental pressure (ICP) and blood perfusion were measured continuously for 2 h. H&E staining and serum index were detected at 0, 1-, 2-, 3-, and 8-h postinjury. The results showed injured hindlimbs swelled, with ICP rising continuously, reaching 30 mmHg in 35 min, peaking at 60-80 min before entering a plateau period, effectively reproducing ACS pressure evolution. The blood perfusion showed a three-step change, negatively correlating with ICP > 30 mmHg. Muscle tissues presented typical time-dependent pathological changes, with progressive myofibrillar rupture, inflammatory cell infiltration, and increases in serum IL-1β, TNF-α, CK, and BUN over time. This study successfully established a new preclinical ACS-like rat model. Compared with previous studies, the ICP in the hindlimbs in this study was spontaneously elevated after crush, leading to secondary injury. Furthermore, a new manometric catheter device was developed to enable continuous monitoring of ICP without repeated fluid injections. The constructed animal model is closer to the clinic, and it lays a solid foundation for ACS pathogenesis.

Purpose To develop and validate an artificial intelligence model based on focused assessment with sonography for trauma (FAST) for the semi-quantitative grading of intra-abdominal hemorrhage resulting from blunt abdominal trauma, particularly for use in prehospital or resource-limited settings. Methods Nine Bama miniature pigs, mean weight (31.46 ± 3.73) kg were enrolled. Graded hemorrhage from 0 to 1000 mL was simulated by infusing 100 mL of autologous arterial blood into the peritoneal cavity at each step. The hemorrhage volume was mapped to 3 grades based on total blood volume (estimated at 65 mL/kg): Grade I (< 15%), Grade II (15% – 30%), and Grade III (> 30%). FAST ultrasound videos were acquired from 6 standard sites: right upper quadrant-1, right upper quadrant-2, left upper quadrant-1, left upper quadrant-2, right pelvic cavity, and left pelvic cavity. The pixel area of hemorrhage was obtained by manually segmenting the frame with the largest fluid collection using ITK-SNAP, and the corresponding scanning depth was recorded. A linear mixed-effects model was used to assess the impact of scanning depth on pixel area. A deep neural network, incorporating class weighting and dynamic probability threshold optimization, was constructed using a multimodal feature set including animal weight, pixel areas and scanning depths from each site, and the total pixel area. A 3-grade classification was performed. The model's performance was evaluated using leave-one-out cross-validation on an animal basis and compared with logistic regression, random forest, gradient boosting decision tree, and support vector machine. Results A total of 797 raw videos were acquired, with 522 videos comprising 87 data groups (each covering 6 sites) included after screening. As hemorrhage volume increased, heart rate and shock index rose, while systolic blood pressure decreased; at 800 mL of hemorrhage, the shock index was 2.31 ± 0.38. The mixed-effects model revealed a significant negative correlation between scanning depth and pixel area (β = -2099.00, SE = 1041.13, z = -2.02, p = 0.044). The proposed model achieved an overall accuracy of 81.19%, outperforming support vector machine (73.77%), gradient boosting decision tree (70.63%), random forest (69.52%), and logistic regression (65.99%). Conclusion In a porcine model of blunt abdominal trauma, a multimodal artificial intelligence approach based on FAST multi-site pixel area features, combined with a deep neural network optimized by class weighting and dynamic probability thresholds, can achieve semi-quantitative grading of intra-abdominal hemorrhage.

INTRODUCTION:Major gastrointestinal bleeding (MGB) is a critical complication of sepsis that is associated with a poor prognosis. We aimed to identify independent predictors of MGB in a large multicenter cohort of septic patients to facilitate clinical risk stratification. MATERIALS AND METHODS:This retrospective study analyzed data collected from four tertiary hospitals in China between 2016 and 2023. Data on patient demographics, comorbidities, infection sources, admission laboratory parameters and treatments were collected. The primary outcome was MGB within 28 d, defined as overt bleeding with a hemoglobin drop >20 g/L or transfusion requirement. RESULTS:Of 10,249 eligible patients, 205 (2.0%) developed MGB. In-hospital mortality was significantly higher in the MGB group compared to the non-MGB group (44.0% vs. 17.0%, p < 0.001). Multivariable analysis identified independent risk factors for MGB: renal dysfunction (aOR 1.61), malignancy or immunosuppression (aOR 1.55), gastrointestinal infection (aOR 1.50), elevated blood urea nitrogen (BUN; aOR 1.03) and older age (aOR 1.01). Conversely, urinary tract infection (aOR 0.29) and higher baseline hemoglobin (HGB) levels (aOR 0.99) were associated with a lower risk. The predictive model achieved an area under the curve (AUC) of 0.702. CONCLUSIONS:Although MGB occurred in only 2.0% of septic patients, it was associated with substantially higher mortality. Older age, renal dysfunction, elevated BUN and gastrointestinal sources were key drivers of risk, while urinary tract infection was associated with a lower risk. These findings underscore the importance of early risk stratification and targeted preventive strategies in high-risk patients to mitigate bleeding events and improve survival.

Tetanus is an acute infectious disease caused by Clostridium tetani, which enters the body through contaminated wounds, proliferates under anaerobic conditions, and produces neurotoxin. Patients with major trauma are at a markedly increased risk of tetanus owing to extensive tissue injury, heavy wound contamination, and trauma-associated impairment of immune function. Existing guidelines and consensus statements on tetanus prophylaxis largely address general open wounds and provide little specific guidance for patients with major trauma, particularly those undergoing emergency life-saving surgery through expedited care pathways or admitted to the intensive care unit. To standardize tetanus prophylaxis in the management of major trauma, the writing committee convened Chinese experts in emergency medicine, trauma surgery, critical care medicine, and immunology to develop this expert consensus based on the best available evidence and current clinical practice in China. The consensus covers the fundamental principles of tetanus prophylaxis in major trauma, damage-control debridement, assessment of immune status, active and passive immunization strategies, management of special injury types, prophylaxis in immunocompromised patients, response to mass-casualty incidents, and preventive strategies under resource-limited conditions, and it aims to provide practical guidance for tetanus prophylaxis during the clinical management of major trauma.

To standardize the key techniques and protocols of prehospital emergency care for major trauma in China, and to improve the quality and efficiency of treatment, a multidisciplinary panel of experts in emergency medicine, trauma care, and prehospital rescue was organized to form an expert committee and a core working group. This initiative was co-sponsored by the Trauma Emergency and Multiple Trauma Group of the Trauma Society of the Chinese Medical Association, the Youth Group of the Disaster Medicine Society of the Chinese Medical Association, and the Combat and Trauma Care Specialty Alliance of Army Hospitals. The group systematically searched databases including PubMed, Embase, Cochrane Library, Web of Science, China Biology, Medicine, China National Knowledge Infrastructure, Wanfang Data, and VIP Information to collect relevant guidelines, systematic reviews, and clinical studies. Based on evidence-based findings and clinical practice scenarios, the consensus was drafted. Multiple rounds of consultation and voting were conducted, and a consensus meeting was held to resolve any disputed items. Ultimately, 16 recommendations were formulated addressing 14 core clinical questions in the prehospital management of severe trauma. The contents cover scene safety, rapid recognition and early warning of severe trauma, control of exsanguinating external hemorrhage (X), airway management (A), breathing management (B), circulation management (C), disability/neurologic assessment (D), exposure and temperature control (E), trauma in special populations (the elderly), immobilization and extrication, transport decision-making, prehospital-to-hospital handover, teamwork, and systematic training. This consensus strongly emphasizes the ''xABCDE'' assessment sequence and recommends key interventions such as the use of commercial tourniquets and wound packing for hemorrhage control, permissive hypotensive resuscitation, early administration of tranexamic acid, and the standardized application of pelvic binders. It also advocates the use of standardized communication models for prehospital-to-hospital handovers. This consensus provides prehospital emergency providers in China with a practical, evidence-based set of key techniques and protocols for severe trauma care, which will help promote the standardization of the prehospital trauma care system, support the development of a high-quality national prehospital trauma care system, and ultimately improve patient outcomes.

Purpose: Early identification of hemorrhagic shock remains a clinical challenge, as traditional vital signs often fail to detect hypovolemia during the compensatory phase. Carotid corrected flow time (CCFt), derived from Doppler ultrasound, has been proposed as a surrogate for stroke volume. However, its precise correlation with quantitative blood loss and its diagnostic value in the early stages of shock remain to be fully elucidated. This study aimed to systematically evaluate the sensitivity and diagnostic efficacy of CCFt in graded hemorrhagic shock through both animal experiments and clinical validation. Methods: The study consisted of 2 phases. In the animal phase, a controlled, graded hemorrhage model was established in 7 Bama miniature pigs. Hemodynamic parameters and carotid ultrasound indices (CCFt, peak systolic velocity, and diameter) were measured at baseline and after cumulative blood loss intervals (0 – 1000 mL). In the clinical phase, a case-control study was conducted comparing 18 patients (admitted between November 2024 and February 2026) with hemorrhagic shock to 36 non-shock controls. Diagnostic accuracy was assessed using receiver operating characteristic curve analysis, and dynamic changes in CCFt were monitored during fluid resuscitation in representative cases. Results: In the animal model, CCFt demonstrated a strong negative linear correlation with quantitative blood loss (R = 0.870, p < 0.001). Notably, CCFt decreased significantly at the early stage of hemorrhage (<300 mL), exhibiting high diagnostic accuracy for early/mild shock (area under the curve (AUC) = 0.900). In the clinical cohort, CCFt was significantly lower in the shock group than in controls (p < 0.001), yielding an AUC of 0.991 for the diagnosis of hemorrhagic shock, with an optimal cutoff of 311 ms. Longitudinal monitoring revealed that changes in CCFt were highly concordant with clinical response to fluid resuscitation and lactate clearance. Conclusion: CCFt is a sensitive, non-invasive indicator of blood volume loss that correlates closely with hemorrhage severity and enables early detection of compensated hemorrhagic shock. CCFt may serve as a valuable adjunctive tool for bedside assessment and precision-guided resuscitation in emergency and critical care settings.

Objective To understand the authentic early postoperative ambulation experiences of older patients with kinesiophobia following total hip arthroplasty, with the aim of developing early rehabilitation intervention programs for this group. Methods This qualitative study employed purposive sampling to recruit 23 patients with kinesiophobia following total hip arthroplasty. Data were collected through semi-structured interviews, and the transcribed texts were analyzed, categorized, and thematically refined using framework analysis. Results Patients assessed early postoperative ambulation through two main dimensions: threats and responses, which were further categorized into seven themes. The threat assessment included perceived severity (pain and risk of re-injury), susceptibility, internal rewards (self-satisfaction and internal burden), and external rewards (social support and external burden). The response assessment included response efficacy (questioning the effectiveness of activities and being influenced by other patients' experiences), self-efficacy (lack of trust in their physical condition, negative impact of social support, and lack of accurate understanding of rehabilitation information), and response cost (overcoming inertia, requiring more family support and medical resources). Conclusion It is essential to improve patients' judgment on the severity and susceptibility of early ambulation, provide as much support as possible, enhance response efficacy and self-efficacy, reduce response costs, and increase motivation and compliance for early ambulation to improve health outcomes for patients.

PURPOSE:Autonomous medical rescue on high-altitude plateaus faces formidable challenges, as extreme environmental conditions, including hypobaric hypoxia, severe cold, and inaccessible terrain, critically degrade human physiological resilience and logistical accessibility. The "platinum 10 min" window becomes even more stringent, demanding rapid triage and precise resource allocation under severe infrastructure deficits. Existing multi-agent reinforcement learning approaches struggle to reconcile discrete tactical maneuvering with the continuous precision required for medical resource allocation, leading to sub-optimal logistics and coarse treatment decisions. METHODS:To address these limitations, we propose ISMRG2 mSAC (injury severity-medical resource collaborative guided multi-agent soft actor-critic), a framework integrating a hybrid discrete-continuous action space with a severity-aware attention mechanism. By leveraging a maximum entropy objective, ISMRG2 mSAC incentivizes diverse exploration strategies, mitigating premature convergence to local optima. The hybrid architecture facilitates a "micro-dosing" strategy, enabling agents to continuously modulate drug administration proportional to injury severity rather than relying on fixed dosages. RESULTS:Extensive evaluations demonstrate that ISMRG2 mSAC achieves a survival rate of 84.2% and a drug efficiency ratio of 0.83, significantly outperforming baselines such as multi-agent deep deterministic policy gradient and independent proximal policy optimization. Qualitative analysis further reveals that agents balance operational agility with safety, adopting a high-speed rescue policy that trades off marginal collision risk for superior mission completion time and logistical sustainability. CONCLUSION:The proposed ISMRG2 mSAC framework effectively bridges the gap between discrete maneuver control and continuous medical resource allocation in extreme high-altitude rescue scenarios. Its severity-aware attention mechanism and hybrid action space enable fine-grained, adaptive treatment decisions, yielding substantial improvements in both casualty survival and drug utilization efficiency over existing multi-agent reinforcement learning methods.

An open pelvic fracture, defined as a pelvic injury where the fracture site communicates with the external environment via the skin or mucous membranes, is frequently associated with concomitant perineal and pelvic organ injuries. The complex anatomy and high susceptibility to contamination in the perineal region contribute to prolonged treatment courses and high mortality rates for these injuries.1,2 Recent literature reports mortality rates as high as 24.4% .3 Existing classification systems for pelvic fractures are primarily based on osseous structural parameters.4 For instance, the Tile classification system guides fracture fixation based on injury mechanism and stability grades but fails to integrate assessments of soft tissue and visceral injuries, creating significant gaps in clinical decision-making.4 However, no universally accepted classification system currently exists specifically for open pelvic fractures with concomitant perineal injuries. The management of these complex injuries necessitates a coordinated approach involving infection control, soft tissue repair, and pelvic stabilization, highlighting the urgent need for a classification standard that integrates anatomical damage with treatment prioritization.5-8 This study retrospectively analyzes 32 cases of open pelvic fractures with severe perineal injury treated at Daping Hospital, Army Medical University, from January 2014 to December 2023, to compare the associations between the Tile, Jones-Powell, and Fu classification systems and complications/prognosis. Ultimately, this research aims to delineate the clinical applicability and limitations of each system, thereby providing an evidence-based foundation for developing a more comprehensive, multidimensional classification framework.

As a leading cause of death worldwide, cardiovascular disease demands more precise monitoring and early warning systems, posing significant challenges to modern healthcare. However, cardiovascular early warning systems often face two major dilemmas: the “black box dilemma” leads to unreliable estimation results due to limited physiological interpretability, and limited robustness across subjects and blood pressure states arises from physiological heterogeneity. This study innovatively maps the methods of semantic extraction and channel modeling to the cardiovascular system, inspired by the 6G network concept of transmitting meaning rather than data under the semantic communication paradigm. It proposes a novel hemodynamic channel-guided cardiovascular parameter estimation paradigm (HemoSC-P). This paradigm adopts a dual-pillar modeling framework: the semantic pillar utilizes a multi-scale convolutional and phase-aware attention architecture to model the non-stationary dynamics of cardiovascular signals, elevating feature alignment from the temporal domain to the physiological domain. The cardiovascular channel, parameterized by the Windkessel circuit model, serves dual roles as a semantic pathway from cardiac contractions to observable physiological signals and as a repository of physiological knowledge. It guides channel-level physiologically deformable attention to achieve inverse estimation of cardiovascular parameters. To validate this paradigm, this study employs non-invasive blood pressure estimation as a representative case study. Validation was conducted on three representative public datasets (UCI-BP, MIMIC-III, PPG-BP). On MIMIC-III, the mean absolute error $\pm$ standard deviation for systolic and diastolic blood pressure reached 3.04 $\pm$ 3.24 mmHg and 2.57 $\pm$ 2.70 mmHg, respectively. Multi-dataset validation indicates that this paradigm surpasses benchmark methods in accuracy and stability while maintaining scalability.

While type 2 diabetes (T2D) disrupts bone metabolism, T2D individuals often exhibit normal or elevated bone mineral density (BMD), complicating the relationship between BMD and cardiovascular disease (CVD). This study aimed to clarify the associations of BMD with CVD and mortality (CVM) in individuals with/without T2D. a retrospective cohort study. T2D and non-type 2 diabetes (NT2D) individuals were selected from the National Health and Nutrition Examination Survey (2005–2018). They were classified into three groups based on femoral neck BMD: T1(< 0.8 g/cm²), T2(0.8–1 g/cm²), and T3(≥ 1 g/cm²). Weighted multivariate analysis examined the association between BMD and CVD/CVM, while restricted cubic spline assessed their nonlinearity between BMD and CVM. The study comprised 10,586 participants (NT2D: 8,006; T2D: 2,580). During a median follow-up of 125 months, CVD accounted for 201 and 128 deaths in NT2D and T2D groups. After multivariate adjustment, lower BMD was associated with higher CVD risk in the both groups, while higher BMD exhibited lower risk. However, higher BMD in T2D group was linked to increased CVD risk in males[ORT3vs.T2: 1.092 (1.089, 1.095)], ≥ 60 years[ORT3vs.T2: 1.160 (1.156, 1.163)], whites[ORT3vs.T2: 1.024 (1.022, 1.027)], or obese individuals[ORT3vs.T2: 1.288 (1.282, 1.294)]. T2D patients also exhibited increased CVM at both lower and higher BMD levels[HRT1vs.T2: 1.457 (1.454, 1.461); HRT3vs.T2: 1.268 (1.263, 1.272)]. Furthermore, NT2D individuals displayed a J-shaped nonlinear relationship between BMD and CVM, whereas T2D patients showed a U-shaped pattern. While low BMD serve as a risk factor for CVD and mortality in NT2D/T2D individuals, high BMD may be associated with elevated cardiovascular risk in T2D patients.

Junctional hemorrhage, particularly in the axillary region, presents a major challenge in trauma care. Early and effective external compression are crucial life-saving maneuvers; however, the potential compression sites for various techniques remain unclear, and the effectiveness of these techniques may vary depending on the region and type of hemorrhage. This study aims to improve the identification of potential compression sites for axillary hemorrhage control in prehospital scenarios. The classification of subclavian‒axillary artery zones 1–4 was determined on the basis of arterial branching patterns, anatomical landmarks, and body‒surface structures. A total of 230 axilla CTA datasets were collected and randomly divided into training datasets (n=184) and validation datasets (n=46). The two‒dimensional depth images were derived from the three‒dimensional CTA array of the body surface via a dimensionality reduction method. The landmark points for zones 1–4 were annotated on the CTA slices and then projected onto the depth images to create labeled images. We developed a modified U‒Net model with a coordinate attention block and compared its accuracy in zone estimation with that of the standard U‒Net. The Intersection over Union (IoU), precision, recall, and Dice coefficient were analyzed. Compared with the standard model, the modified U‒Net improved the mean IoU, precision, recall, and Dice coefficient in zone 1 by 0.03, 0.01, 0.09, and 0.03, respectively. Among the modified U‒Net model, zone 4 yielded the highest accuracy, whereas zone 1 yielded the lowest accuracy. The median Dice coefficients for subclavian‒axillary artery zones 1–4 were 0.58 (0.52‒0.62), 0.81 (0.74‒0.84), 0.83 (0.78‒0.85), and 0.88 (0.86‒0.91), respectively. Among the 46 validation datasets, 5 datasets presented Dice coefficients exceeding 0.59, 0.81, 0.83, and 0.88 for Zones 1–4, respectively. This study proposed a novel four-zone anatomical framework of the subclavian–axillary artery and demonstrated the feasibility of using deep learning–based semantic segmentation on depth images to localize potential compression regions with high accuracy. The region-specific differences in segmentation performance underscore the need for tailored hemorrhage control strategies based on vascular complexity and surface accessibility. These findings provide critical anatomical and technical groundwork for developing intelligent external compression systems and augmented reality (AR)–assisted visualization tools, thereby enhancing the precision and timeliness of axillary hemorrhage control in prehospital trauma care. Not applicable (retrospective imaging study; no clinical trial registration).

Bacterial infection in wounds is one of the critical factors in delayed healing and can lead to life-threatening sepsis in severe cases. The rapid and nondestructive detection of wound bacteria and timely intervention and treatment are of great significance. Hence, a novel CNN-BiGTrans interactive aggregation network (CBIA-Net) is proposed for bacteria detection using hyperspectral imaging (HSI). Specifically, two feature extraction branches BiGTrans_Branch and CNN1D_Branch are designed: the former captures the sequential information and long-distance dependencies of long sequences while acquiring global information, and the latter adequately extracts the key local features of sequences and expands the receptive field. In addition, a cross-branch weighted fusion module (CWFM) is designed to interactively fuse global and local features of spectral sequences to obtain diverse and robust features. Finally, a hyperspectral wound-typical bacteria (HWB) dataset is constructed to evaluate the performance of the proposed method. The experimental results show that the proposed method has better classification performance compared with other models, reaching 97.04%, 97.03%, 97.03%, and 97.03% in the four metrics of precision, recall, accuracy, and F1 -score, respectively. We believe that this method can be used for the rapid detection of other bacteria and has a great potential application in the task of identifying typical bacteria on real wounds.

The "valley of death" symbolizes a critical gap between basic and clinical research. Although significant efforts have been made in the medical field with the assistance of AI, most of these advancements remain at the laboratory research stage. There is still a lack of research aimed at bridging the gap between laboratory experiments and clinical applications. This paper focuses on solving this issue by proposing a novel regression analysis approach for measuring intra-abdominal pressure (IAP). We aim to minimize the gap between swine models and human models. Due to the invasive nature of the IAP measurement process and privacy concerns, human data are limited, which hampers the ability to train high-performance deep learning models. Leveraging the complete life cycle of a swine, from a healthy state to heightened IAP leading to mortality, allows us to simulate all the stages encountered regarding human IAP variations. Specifically, we employ contrastive learning to regulate and place the features in sequential order, followed by a Kullback-Leibler divergence- based domain adaptation technique to transfer the knowledge gained from the swine model to the human model. In the future, we will consider transferring this method to the prediction field. Our proposed method significantly reduces the induced mean absolute error (MAE) from 1.5537 to 0.3614 in comparison with the baseline models, demonstrating the efficacy of our approach.

Background: The impact of dietary carbohydrate intake on bone health remains a subject of controversy, potentially influenced by individuals with diabetic osteoporosis who exhibit normal or elevated bone mineral density (BMD). The cross-sectional study was conducted to explore the association between carbohydrate intake and BMD, osteoporosis and fractures among adults without diabetes, based on the National health and nutrition examination survey (NHANES). Methods: Participants were from the NHANES 2005-2010, excluding individuals with diabetes and those with incomplete data. The association between carbohydrate intake and BMD was analyzed using Spearman correlation, linear regression analysis and subgroup analysis, respectively. The association between carbohydrate intake and osteoporosis/fractures was analyzed using weighted logistic regression analysis. Results: A total of 7275 adult participants were included and their dietary carbohydrate intake was inversely associated with BMD in the total femur [(3 =-0.20 95%CI (-0.30,-0.10); p < 0.001], femoral neck [(3 =-0.10 95%CI (-0.20,-0.00); p = 0.002], and lumbar spine [(3 =-0.10 95%CI (-0.20,-0.00); p = 0.004]. Stratified analysis indicated that individuals aged 65 and over, women, and non-Hispanic whites were more likely to have lower BMD. Furthermore, a higher intake of dietary carbohydrates was associated with an increased risk of osteoporosis [OR = 1.001 95%CI (1.001, 1.001); p < 0.001] and fractures at the hip [OR = 1.005 95%CI (1.005, 1.005); p < 0.001], wrist [OR = 1.001 95%CI (1.001, 1.001), p < 0.001], and spine [OR = 1.003 95%CI(1.003, 1.003); p < 0.001]. Conclusions: A higher carbohydrate diet is associated with lower BMD and a higher risk of osteoporosis and fractures among adults without diabetes, and a higher carbohydrate consumption show a stronger effect in individuals aged 65 and over, women, and non-Hispanic whites.

Endoscopic techniques have been widely used in orthopedic surgery, such as arthroscopy and transforaminal endoscopy, but the application in fracture is rarely reported. We reported a case of a 69-year-old male with pelvic fracture (AO/OTA type B2.1) who underwent successful laparoscopy-assisted pubic ramus plate fixation without auxiliary incision. We designed and applied a separate custom-made lengthening surgical instrument for internal fixation installation suitable for laparoscopic surgery, and the entire reduction and internal fixation installation were performed under laparoscopy. The patient could sit up 1 day after surgery, and the reported pain visual analogue scale score decreased from 5 points before surgery to 1 point. At 2 weeks after surgery, the patient could walk with a single crutch. At 4 weeks after surgery, the Majeed score was 73 points, and at 10 weeks after surgery, the Majeed score increased to 81 points. Twelve weeks after surgery, the patient was able to walk independently without pain, with normal defecation and urination function, and the Majeed score was 87. Laparoscopic surgery is a new strategy for treating pelvic ring fractures. The case proves that full laparoscopic-assisted closed reduction and internal fixation of pelvic fractures is feasible.

This study examined the association of serum total alkaline phosphatase (T-ALP) with bone mineral density (BMD) and osteoporosis prevalence in the general population, and investigated its association with mortality in individuals with osteoporosis, using data from the National Health and Nutrition Examination Survey (NHANES) between 2005 and 2018. Elevated serum T-ALP levels were significantly associated with both reduced BMD and an increased risk of osteoporosis in all participants. Moreover, elevated T-ALP levels were linked to higher all-cause mortality among individuals with osteoporosis during this period. INTRODUCTION:The evidence regarding the association between serum T-ALP, BMD and osteoporosis prevalence in general population is incomplete, and limited evidence is available concerning its association with mortality among individuals with osteoporosis. The study investigated the association of serum T-ALP with BMD and osteoporosis prevalence in the general population, and examined its association with mortality in individuals with osteoporosis. METHODS:All participants were adults from the NHANES (2005-2018), and mortality data were obtained from the National Death Index up to December 31, 2019. Firstly, the association of serum T-ALP with BMD and osteoporosis risk was assessed using linear regression model, subgroup analysis, analysis of covariance and weighted logistic regression model, respectively. Secondly, survival analysis including Kaplan-Meier curves, Cox proportional hazards models, and restricted cubic spline regression models were utilized to analyze the relationship between serum T-ALP levels and mortality risk. RESULTS:The study included 13,724 participants aged 18 to 85 years, and 944 were diagnosed with osteoporosis, among whom 221 died during a median of 133 months follow-up. Totally, elevated serum T-ALP was significantly associated with low BMD in femoral neck and lumbar spine, and the results exhibited consistency across diverse age, genders, races, and BMI subgroups. Moreover, for each 1 SD increase in T-ALP, there was a 0.5% increase in the prevalence of osteoporosis [OR (95%CI): 1.005 (1.005, 1.005), p < 0.001]. Among individuals with osteoporosis, for every 1 SD increase in T-ALP, the all-cause mortality increased by 0.4% [HR (95%CI):1.004 (1.002, 1.006), p < 0.001]. Meanwhile, comparing participants with highest serum T-ALP levels (> 79 IU/L) to those with lowest levels (< 53 IU/L) further raised the prevalence of osteoporosis [OR (95%CI):1.292 (1.021, 1.636), p = 0.033] and all-cause mortality [HR (95% CI):1.232 (1.041, 1.459), p = 0.015]. CONCLUSIONS:Based on a representative sample of US adults, elevated serum T-ALP levels were found to be significantly associated with both reduced BMD and an increased risk of osteoporosis across all participants, as well as with a higher all-cause mortality in individuals with osteoporosis.

Calcium intake is widely recommended, but its association with mortality remains unclear. This study indicated higher levels of calcium intake were associated with lower mortality in American adults. However, a nonlinear association between calcium intake and mortality suggested that excessive calcium intake beyond a certain threshold may increase mortality risk. The study aimed to investigate the association of total, dietary, or supplemental calcium intake with all-cause, cardiovascular, and cancer mortality in American adults. This prospective cohort study used the National Health and Nutrition Examination Survey from 2005 to 2018. Participants were categorized into tertiles based on calcium intake. Risks of all-cause, cancer, or cardiovascular mortality and the dose–response relationship were estimated using weighted Cox proportional hazard regression and restricted cubic splines, with adjustments for demographic characteristics, comorbidities, laboratory parameters, and dietary data. In total, 6172 participants were included (median age: 61 years), and 869 had died (CVD:217, cancer:224) during a median follow-up of 81 months. After adjusting for confounders, higher total calcium(≥ 1660mg/d) [HR, 95