Electroencephalogram (EEG) is a non-invasive, high temporal-resolution technique for monitoring brain activity. However, affected by the volume conduction effect, EEG has a low spatial resolution and is difficult to locate brain neuronal activity precisely. The surface Laplacian (SL) technique obtains the Laplacian EEG (LEEG) by estimating the second-order spatial derivative of the scalp potential. LEEG can reflect the radial current activity under the scalp, with positive values indicating current flow from the brain to the scalp (“source”) and negative values indicating current flow from the scalp to the brain (“sink”). It attenuates signals from volume conduction, effectively improving the spatial resolution of EEG, and is expected to contribute to breakthroughs in neural engineering. This paper provides a systematic overview of the principles and development of SL technology. Currently, there are two implementation paths for SL technology: current source density algorithms (CSD) and concentric ring electrodes (CRE). CSD performs the Laplace transform of the EEG signals acquired by conventional disc electrodes to indirectly estimate the LEEG. It can be mainly classified into local methods, global methods, and realistic Laplacian methods. The global method is the most commonly used approach in CSD, which can achieve more accurate estimation compared with the local method, and it does not require additional imaging equipment compared with the realistic Laplacian method. CRE employs new concentric ring electrodes instead of the traditional disc electrodes, and measures the LEEG directly by differential acquisition of the multi-ring signals. Depending on the structure, it can be divided into bipolar CRE, quasi-bipolar CRE, tripolar CRE, and multi-pole CRE. The tripolar CRE is widely used due to its optimal detection performance. While ensuring the quality of signal acquisition, the complexity of its preamplifier is relatively acceptable. Here, this paper introduces the study of the SL technique in resting rhythms, visual-related potentials, movement-related potentials, and sensorimotor rhythms. These studies demonstrate that SL technology can improve signal quality and enhance signal characteristics, confirming its potential applications in neuroscientific research, disease diagnosis, visual pathway detection, and brain-computer interfaces. CSD is frequently utilized in applications such as neuroscientific research and disease detection, where high-precision estimation of LEEG is required. And CRE tends to be used in brain-computer interfaces, that have stringent requirements for real-time data processing. Finally, this paper summarizes the strengths and weaknesses of SL technology and envisages its future development. SL technology boasts advantages such as reference independence, high spatial resolution, high temporal resolution, enhanced source connectivity analysis, and noise suppression. However, it also has shortcomings that can be further improved. Theoretically, simulation experiments should be conducted to investigate the theoretical characteristics of SL technology. For CSD methods, the algorithm needs to be optimized to improve the precision of LEEG estimation, reduce dependence on the number of channels, and decrease computational complexity and time consumption. For CRE methods, the electrodes need to be designed with appropriate structures and sizes, and the low-noise, high common-mode rejection ratio preamplifier should be developed. We hope that this paper can promote the in-depth research and wide application of SL technology.

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.

In July 2024,the Diagnosis Related Groups(DRG)2.0 is released based on the Notice from the National Healthcare Security Administration on Issuing the DRG 2.0 and Deepening the Relevant Work.Compared with DRG 1.1,version 2.0 was established based on a wider range of suggestions regarding the Adjacent Diagnosis Related Groups(ADRG),Major Comorbidity or Complication(MCC),and Comorbidity or Complication(CC)from various institutions.A list of disease diagnoses and surgical operations that are not used as grouping rules was compiled,and grouping efficacy was further improved by upgrading the algorithms for MCC and CC with the help of AI.Meanwhile,it is necessary to pay more attention to the number of cases of ADRG,the better methods to list the MCC/CC,the suggestions of various doctors and continuously standardize the data and update the grouping scheme of DRG.

Objective:To compare postoperative anal function recovery between laparoscopic subtotal colectomy with antiperistaltic cecorectal anastomosis and laparoscopic total colectomy with ileorectal anastomosis for slow transit constipation.Methods:This multicenter retrospective cohort study enrolled patients meeting the following criteria: (1) severe constipation symptoms (<2 bowel movements/week), absent or insignificant defecation urge, abdominal distension, requiring laxatives to maintain bowel movements or laxatives being ineffective; (2) constipation symptoms for over 5 years, ineffective after >2 years of medical treatment, with strong desire for surgery; (3) significantly prolonged colon transit time (>72 hours) without significant gastric or small intestinal transit dysfunction; (4) no organic colonic lesions confirmed by colonoscopy and abdominal CT. Exclusion criteria: (1) patients undergoing open surgery; (2) exclusion of outlet obstruction constipation (e.g., rectocele, rectal prolapse, puborectalis spasm) by functional defecation MRI; (3) comorbid psychiatric disorders; (4) missing clinical data or loss to follow-up (postoperative follow-up <24 months). Based on these criteria, clinical and follow-up data were collected from 220 patients who underwent either laparoscopic subtotal colectomy with antiperistaltic cecorectal anastomosis (LSC group, n = 115) or laparoscopic total colectomy with ileorectal anastomosis (LTC group, n = 105) for slow transit constipation between January 2013 and December 2022. Subjective anal function (Constipation Severity Score and Wexner Fecal Incontinence Score) and objective anal function (positive rate of rectoanal inhibitory reflex [RAIR] and anorectal manometry) were observed preoperatively and at 6, 12, and 24 months postoperatively. Results:No significant differences were found in baseline characteristics between the two groups (all P >0.05). All surgeries were completed successfully without major significant complications. Subjective anal function assessment: At 24 months postoperatively, Constipation Severity Scores decreased significantly compared to preoperative scores in both groups [LSC group: (25.2±2.8) vs. (2.9±1.8), P <0.001; LTC group: (25.8±2.9) vs. (2.8±1.9), P<0.001]. No significant differences were found between the groups at 6, 12, and 24 months postoperatively (all P>0.05). Wexner Fecal Incontinence Scores at 24 months were significantly lower than those at 6 months in both groups [LSC group: (12.9±1.8) vs. (3.9±2.5), P<0.001; LTC group: (12.6±1.8) vs. (5.4±2.4), P<0.001]. Although no significant difference was found at 6 months ( P = 0.190), the LSC group had significantly lower Wexner scores than the LTC group at 12 and 24 months postoperatively (both P < 0.001). Objective anal function assessment: (1) Positive RAIR rate: Preoperative positive RAIR rates were 33.0% (38/115) in the LSC group and 25.7% (27/105) in the LTC group ( P > 0.05). At 24 months, positive rates increased significantly in both groups [LSC: 66.1% (76/115); LTC: 63.8% (67/105)] compared to preoperative rates (both P<0.001), but no significant differences were found between groups at 6, 12, and 24 months (all P>0.05). (2) Resting pressure (RP) and squeeze pressure (SP): No significant differences were found in preoperative RP and SP between groups (all P>0.05). The LSC group had significantly higher RP and SP than the LTC group at 6 and 12 months postoperatively (all P<0.05), but no significant differences were found at 24 months ( P>0.05). Conclusion:Both laparoscopic subtotal colectomy with antiperistaltic cecorectal anastomosis and laparoscopic total colectomy with ileorectal anastomosis are safe for patients with slow transit constipation. However, laparoscopic subtotal colectomy with antiperistaltic cecorectal anastomosis offers superior postoperative anal function recovery.

Objective:To investigate the application and clinical efficacy of customized, 3D-printed femoral bone marrow stems in the revision of tumor prostheses.Methods:A retrospective analysis was performed for the data of 11 patients (7 males and 4 females) aged 53.1±11.7 years (range, 38-75 years), who underwent 3D-printed customized revision of femoral intramedullary stems due to loosening of femoral tumor prostheses at the 960th Hospital of the Joint Support Force of the PLA from June 2021 to June 2023. The pathological types of tumors associated with the initial surgeries included 4 cases of giant cell tumor of bone, 5 cases of osteosarcoma, 1 case of chondrosarcoma, and 1 case of plasma cell tumor. The tumor was located at the distal femur in 8 cases and the proximal femur in 3 cases. The procedures included 3 initial revisions, 7 secondary revisions, and 1 tertiary revision. The average limb shortening measured 4.6±2.2 cm (range, 2.5-9.0 cm). Prior to revision, all prostheses were fixed with bone cement, revealing enlargement of the femoral medullary cavity and cortical bone thinning. Among them, 5 cases had intramedullary stems permeabilizing the femoral cortex, and 1 case had femoral cleavage fractures. All 11 patients received personalized data for the design and 3D printing of femoral bone marrow stems.Results:The lengths and diameters of the 3D-printed porous femoral bone marrow stems ranged from 80 to 160 mm and 20 to 22 mm, respectively. Ten patients were fitted with cylindrical intramedullary handles, while one received a conical intramedullary handle. A successful revision with the 3D-printed stems was achieved in 10 patients; however, 1 case failed to accommodate the conical handle and was instead revised with a bone cement prosthesis. During the implantation of the intramedullary stems, three patients experienced minor cortical splitting, which was managed with bundling and fixation during the procedure. Immediate stability was attained for all prostheses during surgery, yet postoperative limb shortening did not undergo significant correction. All patients exhibited normal healing of their postoperative incisions. The visual analog scale for limb pain decreased significantly from 8.0±0.8 points before surgery to 1.0±0.4 points three months postoperatively ( t=25.957, P<0.001). By six months after the surgery, none of the patients reported any limb pain. Follow-up data for all 11 patients indicated an average follow-up duration of 25.2±7.5 months (range, 16-36 months), during which limb function improved satisfactorily. The Musculoskeletal Tumor Society (MSTS) score increased from 7.9±1.4 points preoperatively to 20.9±2.7 points at the last follow-up, with this change also being statistically significant ( t=14.229, P<0.001). Imaging evaluations revealed normal lower limb force lines, no rotation or longitudinal displacement of the prosthetic stem, and successful integration with the femur. Conclusion:Personalized intramedullary stems produced through 3D printing demonstrate significant clinical effectiveness in femoral tumor prosthesis revision surgery, making them a viable option for such procedures.

Objective:To investigate the application and clinical efficacy of customized, 3D-printed femoral bone marrow stems in the revision of tumor prostheses.Methods:A retrospective analysis was performed for the data of 11 patients (7 males and 4 females) aged 53.1±11.7 years (range, 38-75 years), who underwent 3D-printed customized revision of femoral intramedullary stems due to loosening of femoral tumor prostheses at the 960th Hospital of the Joint Support Force of the PLA from June 2021 to June 2023. The pathological types of tumors associated with the initial surgeries included 4 cases of giant cell tumor of bone, 5 cases of osteosarcoma, 1 case of chondrosarcoma, and 1 case of plasma cell tumor. The tumor was located at the distal femur in 8 cases and the proximal femur in 3 cases. The procedures included 3 initial revisions, 7 secondary revisions, and 1 tertiary revision. The average limb shortening measured 4.6±2.2 cm (range, 2.5-9.0 cm). Prior to revision, all prostheses were fixed with bone cement, revealing enlargement of the femoral medullary cavity and cortical bone thinning. Among them, 5 cases had intramedullary stems permeabilizing the femoral cortex, and 1 case had femoral cleavage fractures. All 11 patients received personalized data for the design and 3D printing of femoral bone marrow stems.Results:The lengths and diameters of the 3D-printed porous femoral bone marrow stems ranged from 80 to 160 mm and 20 to 22 mm, respectively. Ten patients were fitted with cylindrical intramedullary handles, while one received a conical intramedullary handle. A successful revision with the 3D-printed stems was achieved in 10 patients; however, 1 case failed to accommodate the conical handle and was instead revised with a bone cement prosthesis. During the implantation of the intramedullary stems, three patients experienced minor cortical splitting, which was managed with bundling and fixation during the procedure. Immediate stability was attained for all prostheses during surgery, yet postoperative limb shortening did not undergo significant correction. All patients exhibited normal healing of their postoperative incisions. The visual analog scale for limb pain decreased significantly from 8.0±0.8 points before surgery to 1.0±0.4 points three months postoperatively ( t=25.957, P<0.001). By six months after the surgery, none of the patients reported any limb pain. Follow-up data for all 11 patients indicated an average follow-up duration of 25.2±7.5 months (range, 16-36 months), during which limb function improved satisfactorily. The Musculoskeletal Tumor Society (MSTS) score increased from 7.9±1.4 points preoperatively to 20.9±2.7 points at the last follow-up, with this change also being statistically significant ( t=14.229, P<0.001). Imaging evaluations revealed normal lower limb force lines, no rotation or longitudinal displacement of the prosthetic stem, and successful integration with the femur. Conclusion:Personalized intramedullary stems produced through 3D printing demonstrate significant clinical effectiveness in femoral tumor prosthesis revision surgery, making them a viable option for such procedures.

Postoperative infection of internal fixation of closed fractures the lower limbs in adults represents a devastating complication, characterized by diagnostic challenges, prolonged treatment duration and high disability rates. Current management of these infections faces multiple challenges, such as difficulties in early accurate diagnosis, and various controversies about the treatment plan, leading to poor overall diagnosis and treatment results. To address these issues, based on evidence-based medicine and principles with emphasis on scientific rigor, clinical applicability and innovation, the Trauma Branch of the Chinese Medical Association, Orthopedic Branch of the Chinese Medical Doctor Association, Orthopedics Branch of the Chinese Medical Association, and Trauma Orthopedics and Polytrauma Group of the Resuscitation and Emergency Committee of the Chinese Medical Doctor Association have collaboratively organized a panel of relevant experts to develop the Guideline for diagnosis and treatment of infection after internal fixation of closed lower limb fractures in adults ( version 2025). The guideline proposed 10 recommendations, aiming to provide a foundation for standardized diagnosis and treatment of postoperative infection in adults with closed lower limb fractures.