Objectives: . Idiopathic sudden sensorineural hearing loss (ISSNHL) is a significant cause of hearing loss. Intratympanic steroid injection (ITSI) is commonly used as an initial or salvage treatment; however, the lack of a standardized treatment protocol has resulted in variability in clinical practice. In addition, no efficient prediction model currently exists to support personalized management. Therefore, this study aimed to develop tailored management strategies for ISSNHL using a machine-learning model. Methods: . This retrospective multicenter cohort study was conducted between January 2015 and December 2020, with data analysis performed between January 2021 and March 2024. Patients were selected based on the International Classification of Diseases, 10th Revision criteria for ISSNHL, along with relevant medication and procedure codes. Patients with pure-tone audiogram results not meeting ISSNHL criteria, better initial hearing in the affected ear, an identifiable etiology, no post-treatment audiogram, or delayed treatment (>6 weeks) were excluded. We included 770 patients diagnosed with ISSNHL who received ITSI. The primary outcome was the area under the receiver operating characteristic curve for prediction performance. Recovery status was determined using the last pure-tone audiogram. Modeling was conducted on the Quanta for Medical Care AI platform using five machine-learning algorithms and a nested cross-validation framework, in which feature selection and hyperparameter tuning were performed in the inner folds and model performance was evaluated in the outer folds. Results: . A random forest classifier outperformed the other models in predicting hearing outcomes, achieving an area under the receiver operating characteristic curve of 0.788. Time to ITSI was the most influential treatment-related factor, with ITSI administered within 10 days of hearing loss being associated with better outcomes. This model can be used to provide personalized prognostic estimates under different treatment protocols. Conclusion . The machine-learning-based prediction model facilitates personalized treatment strategies and timely treatment adjustments for ISSNHL, thereby optimizing the likelihood of complete recovery.

ObjectiveTo develop a carbon nanoparticle-manganese dioxide (CN-MnO₂) injection integrating visually identifiable staining and magnetic resonance (MR) T₁/T₂ dual-modal imaging, and to systematically evaluate its physicochemical properties, critical quality attributes, biocompatibility, and efficacy for gastrointestinal marking and localization. MethodsCN-MnO₂ was prepared using a mild liquid-phase in situ redox deposition strategy and characterized by transmission electron microscopy (TEM), dynamic light scattering (DLS), zeta potential analysis, X-ray photoelectron spectroscopy (XPS), and X-ray diffraction (XRD). The pH of CN-MnO₂ was directly measured using a pH meter. The osmolality pressure of CN-MnO₂ was determined by the freezing point depression method. The bacterial endotoxin was detected using the gel-clot Limulus amebocyte lysate test, which did not rely on absorbance readings. The appearance of the CN-MnO₂ injection solution stored at 4 ℃ under light shielding and sealed for 0, 5, and 10 days was observed, and its hydrated particle size, polydispersity index, zeta potential, and pH were also measured. With gadopentetate dimeglumine (Gd-DTPA) as the positive control, different concentration gradients were established, and T₁ and T₂ relaxation times were measured to construct concentration-dependent plots of the longitudinal and transverse relaxation rates (R₁ and R₂) and calculate the corresponding relaxivities (r₁ and r₂), compare relaxivity performance, and determine the working concentration for subsequent in vivo experiments. Cytocompatibility was assessed using the methyl thiazolyl tetrazolium (MTT) assay and live/dead staining. Sixteen male 6-week-old SD rats were randomly assigned to an experimental group and a control group (n=8 per group) and subcutaneously injected with CN-MnO₂ injection at a total Mn concentration of 0.80 mmol/L or an equal volume of normal saline, respectively. Short-term in vivo safety was evaluated by regular body weight monitoring, hematoxylin-eosin (HE) staining of major organs, hematological and serum biochemical analyses, inflammatory cytokine detection, and inductively coupled plasma optical emission spectrometry (ICP-OES) analysis. Eight male 5–6-month-old Bama minipigs were randomly assigned to a CN-MnO₂ group (n=6) and a carbon nanoparticle suspension injection (CNSI) group (n=2). CN-MnO₂ injection or CNSI was injected subserosally into the stomach, small intestine, and colon. Within 5 min after each injection, the maximum staining diameter at each injection site was measured and recorded. MR T1WI/T2WI scans were performed on days 3 and 7 after injection to evaluate MR visualization at the injection sites. At the end of observation, the injection sites and major organs were collected for histopathological evaluation by hematoxylin-eosin staining. ResultsCN-MnO₂ appeared as a black suspension, with no obvious aggregation or sedimentation after standing at room temperature (20-25 ℃) for 15 days. Its average hydrodynamic diameter was approximately (228.7±15.3) nm, the polydispersity index was 0.184±0.021, and the zeta potential was (-24.6±2.8) mV. XPS showed that CN-MnO₂ was mainly composed of C, O, and Mn, with oxygen accounting for approximately 68.82%. The Mn 2p spectrum exhibited two characteristic peaks at 642.4 and 653.1 eV. XRD indicated that low-crystallinity δ-MnO₂ was dispersed within the carbon matrix. The pH was 6.82±0.06, the osmolality was (298.4±6.7) mOsm/kg, and the bacterial endotoxin level was below the detection limit. In vitro MR imaging showed a clear concentration-dependent response, with r₁ and r₂ values of 9.6 L·mmol^-1·s^-1 and 17.2 L·mmol^-1·s^-1, respectively, indicating favorable T₁/T₂ contrast performance superior to that of Gd-DTPA. At 0.80 mmol/L, CN-MnO₂ showed no obvious cytotoxicity in vitro. Short-term safety evaluation in SD rats revealed no obvious inflammatory response, impairment of liver or renal function, or abnormal manganese accumulation in major organs. In Bama minipigs, the mean staining diameters of the CN-MnO₂ group in the stomach, small intestine, and colon were (0.65±0.12) cm, (0.61±0.11) cm, and (0.58±0.10) cm, respectively, with no significant difference among the three injection sites (P>0.05). The overall mean staining diameter was smaller in the CN-MnO₂ group than in the CNSI group (P<0.05). MR examinations showed that the injection sites were identifiable on both day 3 and day 7, with stronger signals observed on day 3. At the end of observation, no obvious pathological abnormalities were observed at the injection sites or in major organs. ConclusionA CN-MnO₂ injection with uniform particle size and stable physicochemical properties can be successfully prepared. This formulation integrates visible gastrointestinal staining with MR T₁/T₂ dual-modal imaging, providing a new strategy for preoperative gastrointestinal marking and localization as well as precise intraoperative resection.

Paraplegia caused by spinal cord injury is a serious neurological complication, for which surgery is currently the main treatment method. Due to different surgical approaches, patients are usually expected to maintain a passive prone position for a long time or switch between the supine and prone positions. Affected by multiple factors such as neurogenic sensory disorders, pathological changes in muscle tone and operative duration, the risk of intraoperative acquired pressure injury (IAPI) is significantly increased. Current clinical prevention strategies for IAPI in these patients predominantly focus on localized pressure relief during positioning, lacking systematic, standardized comprehensive prevention protocols or evidence-based guidelines. To address it, Department of Nursing, Orthopedics Branch, China International Exchange and Promotive Association for Medical and Health Care, Spinal Trauma Professional Committee, Orthopedics Branch, Chinese Medical Doctor Association, Nursing Group of Spine and Spinal Cord Professional Committee of Chinese Association of Rehabilitation Medicine organized experts in relevant fields to formulate Guideline for the prevention of intraoperative acquired pressure injury in paraplegic patients with spinal cord injury ( version 2025), based on evidence-based medical evidence and latest research results and clinical practice at home and abroad. Eleven recommendations were put forward from the aspects of preoperative risk assessment, intraoperative prevention strategies, postoperative handover and monitoring, and supportive mechanisms for IAPI prevention, aiming to standardize the prevention measures and management strategies of IAPI in paraplegic patients with spinal cord injury and accelerate the recovery of patients and improve the therapeutic effect.

Objective To design a whole-process intelligent service platform to enhance services for inpatients.Methods A whole-process intelligent service platform for inpatients was designed with an intelligentized rehabilitation assessment model based on the support vector machine algorithm,which was constructed with the hyper-converged infrastructure,designed with the B/S architecture and developed using the front-end and back-end separation mode.The front end used Vue.js and Ant Design of Vue for user interface construction and JavaScript for data interaction,and the back end was developed with Java.The platform developed was composed of three functional modules for medical service management,intelligent rehabilitation management and system management.Results The platform developed provided whole-process inpatient service before,during and after hospitalization to enhance inpatient rehabilitation.Conclusion The platform developed is able to meet the health management needs of inpatients in terms of treatment-rehabilitation-follow-up,which improve the service quality of hospitals.[Chinese Medical Equipment Journal,2025,46(6):34-41]

Objective To analyze the expression level of triggering receptor expressed on myeloid cells-1(TREM-1)in serum and ascites of patients with cirrhotic ascites,and to investigate its correlation with clinical features and inflammatory markers and its role in the diagnosis of infection and prognostic evaluation.Methods A total of 110 patients with cirrhotic ascites who were hospitalized in The Fifth Hospital of Shijiazhuang from January 2019 to December 2020 were enrolled,and according to the presence or absence of intra-abdominal infection,they were divided into infection group with 72 patients and non-infection group with 38 patients.The patients with infection were further divided into improvement group with 38 patients and non-improvement group with 34 patients.Clinical data and laboratory markers were collected from all patients.Serum and ascites samples were collected,and ELISA was used to measure the level of TREM-1.The independent-samples t test was used for comparison of normally distributed continuous data between two groups;the Mann-Whitney U test was used for comparison of non-normally distributed continuous data between two groups,and the Kruskal-Wallis H test was used for comparison between multiple groups;the chi-square test was used for comparison of categorical data between two groups.A Spearman correlation analysis was used to investigate the correlation between indicators.A multivariate Logistic regression analysis was used to identify the influencing factors for the prognosis of patients with cirrhotic ascites and infection.The receiver operating characteristic(ROC)curve was used to evaluate the diagnostic and prognostic efficacy of each indicator,and the Delong test was used for comparison of the area under the ROC curve(AUC).Results The level of TREM-1 in ascites was significantly positively correlated with that in serum(r=0.50,P<0.001).Compared with the improvement group,the non-improvement group had a significantly higher level of TREM-1 in ascites(Z=-2.391,P=0.017)and serum(Z=-2.544,P=0.011),and compared with the non-infection group,the infection group had a significantly higher level of TREM-1 in ascites(Z=-3.420,P<0.001),while there was no significant difference in the level of TREM-1 in serum between the two groups(P>0.05).The level of TREM-1 in serum and ascites were significantly positively correlated with C-reactive protein(CRP),procalcitonin(PCT),white blood cell count,and neutrophil-lymphocyte ratio(r=0.288,0.344,0.530,0.510,0.534,0.454,0.330,and 0.404,all P<0.05).The ROC curve analysis showed that when PCT,CRP,and serum or ascitic TREM-1 were used in combination for the diagnosis of cirrhotic ascites with infection,the AUCs were 0.715 and 0.740,respectively.The multivariate Logistic regression analysis showed that CRP(odds ratio[OR]=1.019,95%confidence interval[CI]:1.001-1.038,P=0.043)and serum TREM-1(OR=1.002,95%CI:1.000-1.003,P=0.016)were independent risk factors for the prognosis of patients with cirrhotic ascites and infection,and the combination of these two indicators had an AUC of 0.728 in predicting poor prognosis.Conclusion The level of TREM-1 is closely associated with the severity of infection and prognosis in patients with cirrhotic ascites,and combined measurement of TREM-1 and CRP/PCT can improve the diagnostic accuracy of infection and provide support for prognostic evaluation.

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.

Traditional development of small protein scaffolds has relied on display technologies and mutation-based engineering, which limit sequence and functional diversity, thereby constraining their therapeutic and application potential. Protein design tools have significantly advanced the creation of novel protein sequences, structures, and functions. However, further improvements in design strategies are still needed to more efficiently optimize the functional performance of protein-based drugs and enhance their druggability. Here, we extended an evolution-based design protocol to create a novel minibinder, BindHer, against the human epidermal growth factor receptor 2 (HER2). It not only exhibits super stability and binding selectivity but also demonstrates remarkable properties in tissue specificity. Radiolabeling experiments with ^99mTc, ⁶⁸Ga, and ¹⁸F revealed that BindHer efficiently targets tumors in HER2-positive breast cancer mouse models, with minimal nonspecific liver absorption, outperforming scaffolds designed through traditional engineering. These findings highlight a new rational approach to automated protein design, offering significant potential for large-scale applications in therapeutic mini-protein development.