ObjectiveThis paper proposes a novel real-time bedside pulmonary ventilation monitoring method for the diagnosis of chronic obstructive pulmonary disease (COPD), based on electrical impedance tomography (EIT). Four indicators—center of ventilation (CoV), global inhomogeneity index (GI), regional ventilation delay inhomogeneity (RVDI), and the ratio of forced expiratory volume in one second to forced vital capacity (FEV₁/FVC)—are calculated to enable the spatiotemporal assessment of COPD. MethodsA simulation of the respiratory cycles of COPD patients was first conducted, revealing significant differences in certain indicators compared to healthy individuals. The effectiveness of these indicators was then validated through experiments. A total of 93 subjects underwent multiple pulmonary function tests (PFTs) alongside simultaneous EIT measurements. Ventilation heterogeneity under different breathing patterns—including forced exhalation, forced inhalation, and quiet tidal breathing—was compared. EIT images and related indicators were analyzed to distinguish healthy individuals across different age groups from COPD patients. ResultsSimulation results demonstrated significant differences in CoV, GI, FEV₁/FVC, and RVDI between COPD patients and healthy individuals. Experimental findings indicated that, in terms of spatial heterogeneity, the GI values of COPD patients were significantly higher than those of the other two groups, while no significant differences were observed among healthy individuals. Regarding temporal heterogeneity, COPD patients exhibited significantly higher RVDI values than the other groups during both quiet breathing and forced inhalation. Moreover, during forced exhalation, the distribution of FEV₁/FVC values further highlighted the temporal delay heterogeneity of regional lung function in COPD patients, distinguishing them from healthy individuals of various ages. ConclusionEIT technology effectively reveals the spatiotemporal heterogeneity of regional lung function, which holds great promise for the diagnosis and management of COPD.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Objective:To determine the relationship between tibial plateau stresses and malunion by exploring the changes in mechanical conduction in the knee joint after malunion of Hoffa fracture of the tibial plateau.Methods:This study selected 28 knee joint specimens treated with formalin for preservation, half of which were from male and half from female individuals with an age of (51.4±9.5) years. Their structures were intact, and flexion-extension activities normal. X-ray examinations excluded osteoporosis, tuberculosis, and diseases that could have potentially affected bone quality. The knee specimens were divided into a control group (intact tibia) ( n=4) and 6 groups of tibial plateau Hoffa fracture malunion model: 3 vertical malunion groups (groups V1, V2, and V3, with a vertical displacement of 1, 2, and 3 mm, respectively, n=4) and 3 separation malunion groups (groups S3, S5, and S7, with a separation displacement of 3, 5, and 7 mm, respectively), with half males and half females in each group. After a 600N vertical load was applied at passive knee flexions at 0°, 30°, 60°, 90°, and 120°, the stress levels in the medial and lateral compartments of the knee joint were measured using pressure-sensitive films. Results:Under a vertical load of 600 N, when the knee joint was in a neutral position (flexion of 0°), the differences in the medial and lateral tibial plateau stress values were not statistically significant between the malunion models groups and the control group ( P>0.05). When the knee flexion increased to 30°, the medial tibial plateau stress in the V3 and S7 groups was significantly greater than that in the control group ( P<0.05). At a knee flexion of 60°, the medial plateau stress was significantly greater in the V3, S5 and S7 groups than that in the control group, and the differences were significantly greater than the comparisons at a knee flexion of 30° (all P<0.05). When the knee flexion was 90°, the medial plateau stress in the V2, V3, S5 and S7 groups was significantly greater than that in the control group ( P<0.05), but the lateral tibial plateau stress in the V3 group was significantly smaller than that in the control group ( P<0.05). When the knee flexion was further increased to 120°, the differences in the medial and lateral plateau stress values were statistically significant between all the malunion groups and the control group ( P<0.05), and the differences significantly greater than the comparisons at a knee flexion of 90° (all P<0.05). Under a vertical load of 600 N, the differences in the stresses on the medial and lateral plateaus were not statistically significant between the control group and all the malunion groups at a knee flexion of 0° ( P>0.05). When the knee flexion increased to 30°, the difference between the medial and lateral stresses was not statistically significant in the control group ( P>0.05), but was statistically significant in the V3 and S7 groups ( P<0.05). When the knee flexion reached 60°, 90°, and 120°, the differences between the medial and lateral tibial plateau stresses in all the groups were statistically significant ( P<0.05). Conclusions:The peak knee stresses after malunion of Hoffa fracture of the tibial plateau correlate with the severity of malunion and knee flexion angles. The mechanical properties are not significantly different between a mild malunion knee and a normal knee, but a significant displacement (vertical displacement >2 mm and separation displacement ≥5 mm) may increase the peak knee stresses to increase the risk of knee osteoarthritis. When the severity of malunion is certain, an increase in knee flexion angle increases the difference in the peak stress between the medial and lateral tibial plateaus, thus increasing the risk of knee osteoarthritis.

Objective:pH low insertion peptide (pHLIP)-variant 7 (var7)-fluorescein isothiocyanate (FITC) was used to explore an accurate imaging tool that targeted burn wounds to better perform burn debridement.Methods:Twelve rat models of burn wound were established and pHLIP-var7-FITC with different concentrations (0.5, 1.5 and 2.0 mg/ml) were injected from the rat tail vein for in vivo fluorescence imaging. By determining the concentration of fluorescent conjugates to the burn wound, the scope of wound injury necrosis was judged by combining pathological sections, and its residue and toxicity in important organs such as heart, liver, kidneys, and brain were detected. The Kruskal-Wallis rank sum test, Bonferroni correction method and one-way analysis of variance were used for data analysis. Results:Within 24 h, the fluorescence photons per unit area of the burn wound in the group of 0.5 mg/ml, 1.5 mg/ml and 2.0 mg/ml were 1.49(1.31, 1.65), 2.46(1.88, 2.68), 2.77 (1.94, 3.10)×10 7 p·s -1·cm -2·Sr -1, with significant differences in the overall distribution of fluorescence photons ( H=73.55, P<0.001). The fluorescence intensity was stronger in the group with higher concentration, but with no significant difference in the number of fluorescence photons between the group of 1.5 mg/ml and 2.0 mg/ml ( P=0.263, Bonferroni correction method). At 14 time points (0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 5.0, 6.0, 7.0, 8.0, 12, 24 h), there was no significant difference in the overall mean of fluorescence photons ( F=1.04, P=0.419), and the tissue with burn necrosis seen in tissue sections was highly consistent with the fluorescence imaging region. There was no obvious fluorescence residue in the heart, liver, kidney and brain sections. Conclusion:In superficial second-degree burn tissue, pHLIP-var7-FITC can accurately target and gather on the burn wound within 24 h, showing a clear boundary between burn tissue and normal tissue, which can assist clinical surgical debridement to determine the extent of injury.

AIM To establish an HPLC method for the simultaneous content determination of gallic acid,protocatechuic acid,morroniside,loganin,sweroside,paeoniflorin,hypericin,astragalin,salvianolic acid B,salvianolic acid A,epimedin C and icariin in Bushen Huoxue Sanjie Capsules.METHODS The analysis was performed on a 30℃thermostatic Agilent 5 TC-C18 column(250 mm×4.6 mm,5 μm),with the mobile phase comprising of acetonitrile-0.1%phosphoric acid flowing at 1.0 mL/min in a gradient elution manner,and the detection wavelength was set at 240 nm.RESULTS Twelve constituents showed good linear relationships within their own ranges(r≥0.999 8),whose average recoveries were 97.11%-101.14%with the RSDs of 0.60%-2.65%.CONCLUSION This simple,accurate and reproducible method can be used for the quality control of Bushen Huoxue Sanjie Capsules.

Patients with severe trauma require an extremely timely treatment and transfusion plays an irreplaceable role in the emergency treatment of such patients. An increasing number of evidence-based medicinal evidences and clinical practices suggest that patients with severe traumatic bleeding benefit from early transfusion of low-titer group O whole blood or hemostatic resuscitation with red blood cells, plasma and platelet of a balanced ratio. However, the current domestic mode of blood supply cannot fully meet the requirements of timely and effective blood transfusion for emergency treatment of patients with severe trauma in clinical practice. In order to solve the key problems in blood supply and blood transfusion strategies for emergency treatment of severe trauma, Branch of Clinical Transfusion Medicine of Chinese Medical Association, Group for Trauma Emergency Care and Multiple Injuries of Trauma Branch of Chinese Medical Association, Young Scholar Group of Disaster Medicine Branch of Chinese Medical Association organized domestic experts of blood transfusion medicine and trauma treatment to jointly formulate Chinese expert consensus on blood support mode and blood transfusion strategies for emergency treatment of severe trauma patients ( version 2024). Based on the evidence-based medical evidence and Delphi method of expert consultation and voting, 10 recommendations were put forward from two aspects of blood support mode and transfusion strategies, aiming to provide a reference for transfusion resuscitation in the emergency treatment of severe trauma and further improve the success rate of treatment of patients with severe trauma.