Purpose: Metastatic rectal cancer (mRC) is a highly lethal and complex disease that demands a multidisciplinary treatment approach. However, the clinical effectiveness of radiotherapy (RT) for de novo mRC remains controversial and uncertain. Methods: This retrospective cohort study examined medical records from Kaohsiung Chang Gung Memorial Hospital for patients with histologically confirmed de novo mRC diagnosed between January 2015 and December 2020. All patients received standard systemic therapy and radical surgery when feasible. The primary outcome, overall survival (OS), was assessed using the Kaplan-Meier method. Multivariable analysis was performed using a Cox regression model. Results: Among 271 patients included in the analysis, 117 received RT and 154 did not. The median OS was significantly longer in the RT group compared with the non-RT group (27.8 months vs. 21.9 months; P=0.046). Multivariate analysis identified several independent predictors of OS: age ≥65 years (hazard ratio [HR], 1.69; 95% confidence interval [CI], 1.26–2.27; P=0.001), primary tumor resection (HR, 2.62; 95% CI, 1.90–3.61; P<0.001), M1b or M1c disease (HR, 1.97; 95% CI, 1.44–2.69; P<0.001), and receipt of RT (HR, 1.41; 95% CI, 1.02–1.94; P=0.036). Conclusion RT significantly improves OS in patients with mRC, underscoring its role in treatment strategies. These findings support its inclusion in therapeutic protocols and highlight the need for larger, multicenter trials to confirm and extend these results.

Background: Cerebral hypoxia-ischemia impairs brain development in extremely preterm infants and is associated with poor neurological outcomes. Near-infrared spectroscopy (NIRS) is a noninvasive continuous monitoring method for regional cerebral oxygen saturation (rcSO2).Purpose: This study evaluated the clinical feasibility and neurological impact of a neurocritical care bundle that incorporates prolonged multidisciplinary hemodynamic monitoring and a stepwise management algorithm. Methods: Preterm infants with a gestational age (GA) ≤28 weeks or birth weight (BW) ≤1,000 g were prospectively enrolled in a bundle group subjected to NIRS for rcSO2, electrical cardiometry for cardiac output, and daily brain and cardiac echography during the first 72 hours of life. Monitoring was repeated weekly in the first month and then monthly until discharge or the term-equivalent age (TEA) was reached. We implemented a stepwise management algorithm for treating cerebral hypoxia. The primary outcome was a composite of mortality and adverse neurological events (structural abnormalities or electroencephalogram-confirmed seizures) before discharge. The secondary outcomes were the physiological pattern of rcSO2 within the initial 72 hours and up to discharge or TEA. Results: Thirty preterm infants (GA, 27.1±2.0 weeks; BW, 830±225 g) were enrolled in the bundle group. The mean time-averaged rcSO2 (66.8%±10.3%) was not associated with GA or BW. However, postnatal age appeared to influence physiological rcSO2 changes, given that rcSO2 values were higher during the initial 72 hours than at subsequent intervals. Seven infants (23.3%) had poor outcomes and significantly lower time-averaged rcSO2 (51.1% [50.0%–65.2%] vs. 71.8% [67.1%–73.1%], P=0.002). Multivariate regression indicated that a lower rcSO2 was an independent risk factor, and a 65% threshold showed an optimal predictive value for poor outcomes. Conclusion The neurocritical care bundle helped identify preterm infants at risk of cerebral hypoxia, and lower rcSO2 was an independent risk factor for composite mortality and adverse neurological outcomes.

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.

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

This study aims to explore the effects and mechanisms of the traditional Chinese medicine Cordyceps sinensis(CS) in ameliorating heart aging and injury in mice based on animal experiments and network pharmacology. A mouse model of heart aging was established by continuously subcutaneous injection of D-galactose(D-gal). Thirty mice were randomly assigned into a normal group, a model group, a low-dose CS(CS-L) group, a high-dose CS(CS-H) group, and a vitamin E(VE) group. Mice in these groups were administrated with normal saline, different doses of CS suspension, or VE suspension via gavage daily. After 60 days of treatment with D-gal and various drugs, all mice were euthanized, and blood and heart tissue samples were collected for determination of the indicators related to heart aging and injury in mice. Experimental results showed that both high and low doses of CS and VE ameliorated the aging phenotype, improved the heart index and myocardial enzyme spectrum, restored the expression levels of proteins associated with cell cycle arrest and senescence-associated secretory phenotypes(SASP), and alleviated the fibrosis and histopathological changes of the heart tissue in model mice. From the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform(TCMSP),259 active ingredients of CS were retrieved. From Gene Cards and OMIM, 2 568 targets related to heart aging were identified, and 133common targets shared by CS and heart aging were obtained. The Gene Ontology(GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes( KEGG) pathway enrichment revealed that the pathways related to heart aging involved oxidative stress,apoptosis, inflammation-related signaling pathways, etc. The animal experiment results showed that both high and low doses of CS and VE ameliorated oxidative stress and apoptosis in the heart tissue to varying degrees in model mice. Additionally, CS-H and VE activated the nuclear factor E2-related factor 2(Nrf2)/heme oxygenase-1(HO-1) pathway and inhibited the expression of key proteins in the nuclear factor-κB(NF-κB) pathway in the heart tissue of model mice. In conclusion, this study demonstrated based on network pharmacology and animal experiments that CS may alleviate heart aging and injury in aging mice by reducing oxidative stress,apoptosis, and inflammation in the heart via the Nrf2/HO-1/NF-κB pathway.
Animals ; Cordyceps/chemistry* ; Mice ; NF-E2-Related Factor 2/genetics* ; NF-kappa B/genetics* ; Aging/genetics* ; Male ; Signal Transduction/drug effects* ; Network Pharmacology ; Drugs, Chinese Herbal/pharmacology* ; Heme Oxygenase-1/genetics* ; Heart/drug effects* ; Humans ; Myocardium/metabolism* ; Membrane Proteins/genetics*

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: A new three-dimensional(3D) classification of posterior cruciate ligament (PCL) tibial avulsion fracture based on computed tomography(CT) features was established and the significance in clinical treatment was explored in this study. METHODS: From May 2013 to November 2023, 43 cases of PCL tibial avulsion fracture in the Second Hospital of Jilin University were analyzed retrospectively, including 29 males and 14 females, aged (34.3±8.5) years. According to traditional Meyers and McKeever classification, 3 cases were typeⅠ;2 cases of typeⅡ;38 cases were type Ⅲ. Based on the characteristics of CT images, 43 patients were given specific treatment strategies and followed up to evaluate the curative effect. According to the degree of fracture displacement, involved range and the integrity of fracture block demonstrated by CT images, the new three-dimensional classification of PCL avulsion fracture was established. Kappa coefficient was used for consistency test. RESULTS: A new 3D classification of PCL tibial avulsion fracture was established. TypeⅠwas the non-displaced fracture (displacement degree ≤3 mm), in which typeⅠa was the avulsion range limited in the posterior intercondylar fossa, and Ib was the avulsion range beyond the posterior intercondylar fossa. TypeⅡrepresented the displaced fracture in the posterior intercondylar fossa (avulsion limited to the posterior intercondylar fossa and fracture displacement>3 mm), in which typeⅡa represented a slight displacement with a intact broken block and the posterior elevation of the avulsion (hinge mechanism), typeⅡb represented the complete separation of fracture ends with a intact fracture block, and typeⅡc was the comminuted fracture. Type Ⅲ was the displaced fracture beyond the posterior intercondylar fossa (avulsion involving the articular surface of the tibial plateau or the intercondylar ridge and the degree of displacement > 3 mm), among which type Ⅲa was the simple fracture with intact broken block, type Ⅲb represented the comminuted fracture, and type Ⅲc was the complex fracture with tibial plateau fracture. According to this new 3D classification, 43 patients were classified as type Ia in 2 cases and typeⅠb in 1 case;typeⅡa in 2 cases, typeⅡb in 15 cases and typeⅡc in 7 cases;type Ⅲa in 2 cases, type Ⅲb in 5 cases and type Ⅲc in 9 cases. All the 43 cases in this study achieved bone union. At the last follow-up, according to the hospital for special surgery knee score(HSS)evaluation system for the knee joint function, 27 cases were excellent, 11 cases were good, 5 cases were fair. The average Kappa value of inter-observer reliability in the first stage was 0.793, and the second stage was 0.855. The average Kappa value of the whole stage was 0.839, indicating high level of consistency. The average Kappa value of intra-observer reliability was 0.893, indicating high level of consistency. CONCLUSION The 3D classification of PCL tibial avulsion fracture is intuitive, demonstrating a high level of reliability. It has a certain guiding significance for the selection of clinical treatment methods, and it is suggested to be promoted and applied as a new classification system in clinical practice.
Humans ; Male ; Female ; Posterior Cruciate Ligament/surgery* ; Adult ; Tibial Fractures/classification* ; Tomography, X-Ray Computed ; Middle Aged ; Retrospective Studies ; Fractures, Avulsion/classification* ; Imaging, Three-Dimensional ; Young Adult