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.

Digital intraoral scanning is a hot topic in the field of oral digital technology.In recent years,digital intra-oral scanning has gradually become the mainstream technology in orthodontics,prosthodontics,and implant dentistry.The precision of digital intraoral scanning and the accuracy and stitching of data collection are the keys to the success of the impression.However,the operators are less familiar with the intraoral scanning characteristics,imaging process-ing,operator scanning method,oral tissue specificity of the scanned object,and restoration design.Thus far,no unified standard and consensus on digital intraoral scanning technology has been achieved at home or abroad.To deal with the problems encountered in oral scanning and improve the quality of digital scanning,we collected common expert opin-ions and sought to expound the causes of scanning errors and countermeasures by summarizing the existing evidence.We also describe the scanning strategies under different oral impression requirements.The expert consensus is that due to various factors affecting the accuracy of digital intraoral scanning and the reproducibility of scanned images,adopting the correct scanning trajectory can shorten clinical operation time and improve scanning accuracy.The scanning trajec-tories mainly include the E-shaped,segmented,and S-shaped methods.When performing fixed denture restoration,it is recommended to first scan the abutment and adjacent teeth.When performing fixed denture restoration,it is recommend-ed to scan the abutment and adjacent teeth first.Then the cavity in the abutment area is excavated.Lastly,the cavity gap was scanned after completing the abutment preparation.This method not only meets clinical needs but also achieves the most reliable accuracy.When performing full denture restoration in edentulous jaws,setting markers on the mucosal tissue at the bottom of the alveolar ridge,simultaneously capturing images of the vestibular area,using different types of scanning paths such as Z-shaped,S-shaped,buccal-palatal and palatal-buccal pathways,segmented scanning of dental arches,and other strategies can reduce scanning errors and improve image stitching and overlap.For implant restora-tion,when a single crown restoration is supported by implants and a small span upper structure restoration,it is recom-mended to first pre-scan the required dental arch.Then the cavity in the abutment area is excavated.Lastly,scanning the cavity gap after installing the implant scanning rod.When repairing a bone level implant crown,an improved indi-rect scanning method can be used.The scanning process includes three steps:First,the temporary restoration,adjacent teeth,and gingival tissue in the mouth are scanned;second,the entire dental arch is scanned after installing a standard scanning rod on the implant;and third,the temporary restoration outside the mouth is scanned to obtain the three-di-mensional shape of the gingival contour of the implant neck,thereby increasing the stability of soft tissue scanning around the implant and improving scanning restoration.For dental implant fixed bridge repair with missing teeth,the mobility of the mucosa increases the difficulty of scanning,making it difficult for scanners to distinguish scanning rods of the same shape and size,which can easily cause image stacking errors.Higher accuracy of digital implant impres-sions can be achieved by changing the geometric shape of the scanning rods to change the optical curvature radius.The consensus confirms that as the range of scanned dental arches and the number of data concatenations increases,the scanning accuracy decreases accordingly,especially when performing full mouth implant restoration impressions.The difficulty of image stitching processing can easily be increased by the presence of unstable and uneven mucosal mor-phology inside the mouth and the lack of relatively obvious and fixed reference objects,which results in insufficient ac-curacy.When designing restorations of this type,it is advisable to carefully choose digital intraoral scanning methods to obtain model data.It is not recommended to use digital impressions when there are more than five missing teeth.

The pathogenesis of osteoarthritis (OA) is related to a variety of factors such as mechanical overload, metabolic dysfunction, aging, etc., and is a group of total joint diseases characterized by intra-articular chondrocyte apoptosis, cartilage fibrillations, synovial inflammation, and osteophyte formation. At present, the treatment methods for osteoarthritis include glucosamine, non-steroidal anti-inflammatory drugs, intra-articular injection of sodium hyaluronate, etc., which are difficult to take effect in a short period of time and require long-term treatment, so the patients struggle to adhere to doctor’s advice. Some methods can only provide temporary relief without chondrocyte protection, and some even increase the risk of cardiovascular disease and gastrointestinal disease. In the advanced stages of OA, patients often have to undergo joint replacement surgery due to pain and joint dysfunction. Mitochondrial dysfunction plays an important role in the development of OA. It is possible to improve mitochondrial biogenesis, quality control, autophagy balance, and oxidative stress levels, thereby exerting a protective effect on chondrocytes in OA. Therefore, compared to traditional treatments, improving mitochondrial function may be a potential treatment for OA. Here, we collected relevant literature on mitochondrial research in OA in recent years, summarized the potential pathogenic factors that affect the development of OA through mitochondrial pathways, and elaborated on relevant treatment methods, in order to provide new diagnostic and therapeutic ideas for the research field of osteoarthritis.

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.

Objective:In chronic hepatitis B (CHB) patients with previous 96-week treatment with tenofovir amibufenamide (TMF) or tenofovir disoproxil fumarate (TDF), we investigated the efficacy of sequential TMF treatment from 96 to 144 weeks.Methods:Enrolled subjects who were previously assigned (2:1) to receive either 25 mg TMF or 300 mg TDF with matching placebo for 96 weeks received extended or switched TMF treatment for 48 weeks. Efficacy was evaluated based on virological, serological, biological parameters, and fibrosis staging. Statistical analysis was performed using the McNemar test, t-test, or Log-Rank test according to the data. Results:593 subjects from the initial TMF group and 287 subjects from the TDF group were included at week 144, with the proportions of HBV DNA<20 IU/ml at week 144 being 86.2% and 83.3%, respectively, and 78.1% and 73.8% in patients with baseline HBV DNA levels ≥8 log10 IU/ml. Resistance to tenofovir was not detected in both groups. For HBeAg loss and seroconversion rates, both groups showed a further increase from week 96 to 144 and the 3-year cumulative rates of HBeAg loss were about 35% in each group. However, HBsAg levels were less affected during 96 to 144 weeks. For patients switched from TDF to TMF, a substantial further increase in the alanine aminotransferase (ALT) normalization rate was observed (11.4%), along with improved FIB-4 scores.Conclusion:After 144 weeks of TMF treatment, CHB patients achieved high rates of virological, serological, and biochemical responses, as well as improved liver fibrosis outcomes. Also, switching to TMF resulted in significant benefits in ALT normalization rates (NCT03903796).

Objective:In chronic hepatitis B (CHB) patients with previous 96-week treatment with tenofovir amibufenamide (TMF) or tenofovir disoproxil fumarate (TDF), we investigated the safety profile of sequential TMF treatment from 96 to 144 weeks.Methods:Enrolled subjects that previously assigned (2:1) to receive either 25 mg TMF or 300 mg TDF with matching placebo for 96 weeks received extending or switching TMF treatment for 48 weeks. Safety profiles of kidney, bone, metabolism, body weight, and others were evaluated.Results:666 subjects from the initial TMF group and 336 subjects from TDF group with at least one dose of assigned treatment were included at week 144. The overall safety profile was favorable in each group and generally similar between extended or switched TMF treatments from week 96 to 144. In subjects switching from TDF to TMF, the non-indexed estimated glomerular filtration rate (by non-indexed CKD-EPI formula) and creatinine clearance (by Cockcroft-Gault formula) were both increased, which were (2.31±8.33) ml/min and (4.24±13.94) ml/min, respectively. These changes were also higher than those in subjects with extending TMF treatment [(0.91±8.06) ml/min and (1.30±13.94) ml/min]. Meanwhile, switching to TMF also led to an increase of the bone mineral density (BMD) by 0.75% in hip and 1.41% in spine. On the other side, a slight change in TC/HDL ratio by 0.16 (IQR: 0.00, 0.43) and an increase in body mass index (BMI) by (0.54±0.98) kg/m 2 were oberved with patients switched to TMF, which were significantly higher than that in TMF group. Conclusion:CHB patients receiving 144 weeks of TMF treatment showed favorable safety profile. After switching to TMF, the bone and renal safety was significantly improved in TDF group, though experienceing change in metabolic parameters and weight gain (NCT03903796).