ObjectiveThis study leverages structural data from antigen-antibody complexes of the influenza A virus neuraminidase (NA) protein to investigate the spatial recognition relationship between the antigenic epitopes and antibody paratopes. MethodsStructural data on NA protein antigen-antibody complexes were comprehensively collected from the SAbDab database, and processed to obtain the amino acid sequences and spatial distribution information on antigenic epitopes and corresponding antibody paratopes. Statistical analysis was conducted on the antibody sequences, frequency of use of genes, amino acid preferences, and the lengths of complementarity determining regions (CDR). Epitope hotspots for antibody binding were analyzed, and the spatial structural similarity of antibody paratopes was calculated and subjected to clustering, which allowed for a comprehensively exploration of the spatial recognition relationship between antigenic epitopes and antibodies. The specificity of antibodies targeting different antigenic epitope clusters was further validated through bio-layer interferometry (BLI) experiments. ResultsThe collected data revealed that the antigen-antibody complex structure data of influenza A virus NA protein in SAbDab database were mainly from H3N2, H7N9 and H1N1 subtypes. The hotspot regions of antigen epitopes were primarily located around the catalytic active site. The antibodies used for structural analysis were primarily derived from human and murine sources. Among murine antibodies, the most frequently used V-J gene combination was IGHV1-12*01/IGHJ2*01, while for human antibodies, the most common combination was IGHV1-69*01/IGHJ6*01. There were significant differences in the lengths and usage preferences of heavy chain CDR amino acids between antibodies that bind within the catalytic active site and those that bind to regions outside the catalytic active site. The results revealed that structurally similar antibodies could recognize the same epitopes, indicating a specific spatial recognition between antibody and antigen epitopes. Structural overlap in the binding regions was observed for antibodies with similar paratope structures, and the competitive binding of these antibodies to the epitope was confirmed through BLI experiments. ConclusionThe antigen epitopes of NA protein mainly ditributed around the catalytic active site and its surrounding loops. Spatial complementarity and electrostatic interactions play crucial roles in the recognition and binding of antibodies to antigenic epitopes in the catalytic region. There existed a spatial recognition relationship between antigens and antibodies that was independent of the uniqueness of antibody sequences, which means that antibodies with different sequences could potentially form similar local spatial structures and recognize the same epitopes.

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.

This paper discusses the mechanism of acupuncture for diabetes mellitus from the perspective of molecular biology, aiming to reveal the potential rules of restoring the balance in the body and fighting against diabetes mellitus (DM). By searching the basic research literature of acupuncture treatment for DM, it is found that the molecular biological mechanism of acupuncture treatment for DM is closely related to the regulation of insulin signaling pathway, the modulation of inflammatory response, the protection and regeneration of islet β-cells, fat metabolism and energy balance. It points out that there are few studies of acupuncture on the type 1 diabetes mellitus and the large-scale randomized controlled trials, as well as the studies on the upstream regulation mechanism, the specific cellular molecules and the interaction mechanism of various molecules. It needs to deepen the multi-level, multi-target and multi-dimensional exploration on the molecular biological mechanism of acupuncture for diabetes mellitus.
Humans ; Acupuncture Therapy ; Diabetes Mellitus/genetics* ; Animals ; Insulin/metabolism* ; Signal Transduction

OBJECTIVE: To analyze the difference in clinical efficacy of minimally invasive transforaminal lumbar interbody fusion (MIS-TLIF) under Quadrant channel system combined with microscope and percutaneous pedicle screw in the treatment of degenerative lumbar spondylolisthesis. METHODS: A total of 114 patients with single-segment degenerative lumbar spondylolisthesis from June 2015 to February 2019, were divided into three groups according to the surgical methods, such as the MIS-TLIF under the microscope surgery group ( microscope group), MIS-TLIF combined with percutaneous pedicle screw technique surgery group(percutaneous group) and posterior lumbar interbody fusion surgery group (open group). In the microscope group, there were 12 males and 26 females, aged from 42 to 83 years with an average of (63.29±9.09) years. In the percutaneous group, there were 16 males and 22 females, aged from 45 to 82 years with an average of (63.37±7.50) years. In the open group, there were 12 males and 26 females, aged from 51 to 82 years with an average of (63.76±8.21) years. The general conditions of operation, such as operation time, intraoperative blood loss, postoperative drainage, length of surgical incision, frequency of intraoperative fluoroscopy and postoperative time of lying in bed were recorded to analyze the differences in surgical related indicators. Visual analogue scale (VAS) of waist and leg pain in preoperative and postoperative period (3 days, 3 months, 6 months and 12 months) were recorded to evaluate pain remission;Oswestry Disability Index(ODI), Japanese Orthopaedic Association (JOA) score were recorded to evaluate the recovery of waist and leg function on preoperative and postoperative 12 months. The lumbar spondylolisthesis rate and intervertebral height at 12 months after operation were recorded to evaluate the reduction of spondylolisthesis. The Siepe intervertebral fusion standard was used to analyze the intervertebral fusion rate at 12 months after operation. RESULTS: ①All 114 patients were followed up more than 1 year, and no complications related to incision infection occurred. In the microscope group, there was 1 case of subcutaneous effusion 8 days after operation. After percutaneous puncture and drainage, waist compression, and then the healing was delayed. In the percutaneous group, 2 cases of paravertebral muscle necrosis occurred on the side of decompression, and the healing was delayed after debridement. In open group, there was 1 case of intraoperative dural tear, which was packed with free adipose tissue during the operation. There was no postoperative cerebrospinal fluid leakage and other related complications.① Compared with microscope group, percutaneous group increased in operation time, intraoperative blood loss, postoperative wound drainage, surgical incision length, intraoperative fluoroscopy times, and postoperative bed rest time. In open group, intraoperative blood loss, postoperative wound drainage, surgical incision length, and postoperative bed rest time increased, but the intraoperative fluoroscopy time decreased. Compared with percutaneous group, the intraoperative blood loss, wound drainage, surgical incision length, and postoperative bed rest time in open group increased, but operative time and the intraoperative fluoroscopy time decreased(P<0.05). ②ODI and JOA scores of the three groups at 12 months after operation were improved compared with those before operation (P<0.05), but there was no significant difference between the three group(P>0.05). ③Compared with microscope group, the VAS of low back pain in percutaneous group increased at 3 days after operation, and VAS of low back pain in open group increased at 3 days, and 12 month after operation. Compared with percutaneous group, the VAS low back pain score of the open group increased at 3 months after operation (P<0.05). ④ The lumbar spondylolisthesis rate of the three groups of patients at 12 months afrer operation was decreased compared with that before operation(P<0.05), and the intervertebral heigh was increased compared with that before operation(P<0.05), however, there was no significant difference among three groups at 12 months afrer operation(P>0.05). ⑤ There was no significant difference between three groups in the lumbar fusion rate at 12 months afrer operation(P>0.05). CONCLUSION The MIS-TLIF assisted by microscope and the MIS-TLIF combined with percutaneous pedicle screw are safe and effective to treat the degenerative lumbar spondylolisthesis with single-segment, and the MIS-TLIF assisted by microscope may be more invasive, cause less blood loss and achieve better clinical efficacy.
Blood Loss, Surgical ; Case-Control Studies ; Female ; Humans ; Low Back Pain ; Lumbar Vertebrae/surgery* ; Male ; Minimally Invasive Surgical Procedures/methods* ; Postoperative Hemorrhage ; Retrospective Studies ; Spinal Fusion/methods* ; Spondylolisthesis/surgery* ; Surgical Wound ; Treatment Outcome

Objective:To explore the prognostic indicators and influencing factors of neonatal asphyxia in Tibet.Methods:From March 2019 to March 2020, the clinical data of 126 asphyxiated newborns admitted to the neonatology department of Lhasa People's Hospital were retrospectively studied. According to the prognosis, they were divided into good prognosis group and poor prognosis group, and the clinical characteristics of the two groups were compared. The predictive value of Apgar score after birth, arterial blood gas analysis within 6 hours [pH, alkali residue (BE)] and their combined detection on the prognosis of asphyxiated newborns was analyzed. Logistic regression analysis was used to analyze the related factors affecting the prognosis of asphyxiated newborns.Results:31 cases (24.6%) in the poor prognosis group and 95 cases (75.4%) in the good prognosis group. The Apgar score at 1, 5 and 10 minutes after birth and pH and BE within 6 hours in the poor prognosis group were significantly lower than those in the good prognosis group (all P<0.05). The incidence of meconium stained amniotic fluid, fetal intrauterine distress, brain damage, lung damage, myocardial damage, kidney damage, abnormal coagulation function and multiple organ damage after asphyxia in the good prognosis group were significantly higher than those in the good prognosis group (all P<0.05). The Apgar score at the 1, 5 and 10 minutes after birth combined with arterial blood gas analysis (pH and BE) within 6 hours after birth had high predictive value for the prognosis of asphyxiated newborns, with the area under receiver operating characteristic (ROC) curve of 0.79, sensitivity of 68.4% and specificity of 90.3%. Multivariate regression analysis showed that meconium stained amniotic fluid ( OR=4.501, 95% CI: 1.262-16.056), lung damage ( OR=5.004, 95% CI: 1.007-24.866) and brain damage ( OR=10.786, 95% CI: 2.726-42.673) were independent risk factors affecting the prognosis of neonatal asphyxia ( P<0.05). Conclusions:In Tibet, there are many factors affecting the poor prognosis of neonatal asphyxia. High attention and intervention should be given to mothers in perinatal period and asphyxiated newborns in order to reduce the incidence of poor prognosis of neonatal asphyxia.

In order to provide efficient medical care to atrial fibrillation patients in the community, the Huamu Community Health Service Center in association with its medical consortium, Renji Hospital have developed a novel atrial fibrillation management system. With the collaboration of general practitioners and specialist team from the tertiary hospital, a special clinic for atrial fibrillation has been set up in the community health service center, which is based on the internet technology and the medical consortium platform. This article introduces the development of this novel system and the initial outcome of the measures, to provide a reference for the management of atrial fibrillation patients in the community.