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.

BACKGROUND: Postoperative pulmonary complications (PPCs) are major adverse events in neurosurgical patients. This study aimed to develop and validate machine learning models predicting PPCs after neurosurgery. METHODS: PPCs were defined according to the European Perioperative Clinical Outcome standards as occurring within 7 postoperative days. Data of cases meeting inclusion/exclusion criteria were extracted from the anesthesia information management system to create three datasets: The development (data of Huashan Hospital, Fudan University from 2018 to 2020), temporal validation (data of Huashan Hospital, Fudan University in 2021) and external validation (data of other three hospitals in 2023) datasets. Machine learning models of six algorithms were trained using either 35 retrievable and plausible features or the 11 features selected by Lasso regression. Temporal validation was conducted for all models and the 11-feature models were also externally validated. Independent risk factors were identified and feature importance in top models was analyzed. RESULTS: PPCs occurred in 712 of 7533 (9.5%), 258 of 2824 (9.1%), and 207 of 2300 (9.0%) patients in the development, temporal validation and external validation datasets, respectively. During cross-validation training, all models except Bayes demonstrated good discrimination with an area under the receiver operating characteristic curve (AUC) of 0.840. In temporal validation of full-feature models, deep neural network (DNN) performed the best with an AUC of 0.835 (95% confidence interval [CI]: 0.805-0.858) and a Brier score of 0.069, followed by Logistic regression (LR), random forest and XGBoost. The 11-feature models performed comparable to full-feature models with very close but statistically significantly lower AUCs, with the top models of DNN and LR in temporal and external validations. An 11-feature nomogram was drawn based on the LR algorithm and it outperformed the minimally modified Assess respiratory RIsk in Surgical patients in CATalonia (ARISCAT) and Laparoscopic Surgery Video Educational Guidelines (LAS VEGAS) scores with a higher AUC (LR: 0.824, ARISCAT: 0.672, LAS: 0.663). Independent risk factors based on multivariate LR mostly overlapped with Lasso-selected features, but lacked consistency with the important features using the Shapley additive explanation (SHAP) method of the LR model. CONCLUSIONS: The developed models, especially the DNN model and the nomogram, had good discrimination and calibration, and could be used for predicting PPCs in neurosurgical patients. The establishment of machine learning models and the ascertainment of risk factors might assist clinical decision support for improving surgical outcomes. TRIAL REGISTRATION ChiCTR 2100047474; https://www.chictr.org.cn/showproj.html?proj=128279 .
Adult ; Aged ; Female ; Humans ; Male ; Middle Aged ; Algorithms ; Lung Diseases/etiology* ; Machine Learning ; Neurosurgical Procedures/adverse effects* ; Postoperative Complications/diagnosis* ; Risk Factors ; ROC Curve

OBJECTIVE: To investigate effectiveness of arthroscopic superior capsular reconstruction using a "sandwich" patch combined with platelet-rich plasma (PRP) injection in treating massive irreparable rotator cuff tears. METHODS: A clinical data of 15 patients (15 sides) with massive irreparable rotator cuff tears, who were admitted between September 2020 and March 2023 and met the selective criteria, was retrospectively analyzed. There were 8 males and 7 females with an average age of 62.1 years (range, 40-80 years). The rotator cuff tears were caused by trauma in 7 cases and other reasons in 8 cases. The disease duration ranged from 5 to 25 months, with an average of 17.7 months. According to the Hamada grading, the rotator cuff tears were rated as grade 1 in 2 cases, grade 2 in 8 cases, and grade 3 in 5 cases. All patients were underwent superior capsular reconstruction using the "sandwich" patches (autologous fascia lata+polypropylene patch+autologous fascia lata) combined with PRP injection on patches. The pre- and post-operative active range of motion (ROM) of the shoulder joint, American Shoulder and Elbow Surgeons (ASES) score, Constant-Murley score, University of California, Los Angeles Shoulder Rating Scale (UCLA) score, and visual analogue scale (VAS) score were recorded. The subacromial space was measured on the imaging and rotator cuff integrity was assessed based on Sugaya grading. RESULTS: All incisions healed by first intention after operation without any complications such as infection. All patients were followed up 12-18 months (mean, 14.4 months). At last follow-up, the active ROMs of flexion, abduction, external rotation, internal rotation of the shoulder joint, subacromial space, ASES score, Constant-Murley score, and UCLA score increased, and VAS score decreased, showing significant differences when compared with preoperative values ( P<0.05). There was no significant difference in the Sugaya grading between last follow-up and immediately after operation ( P>0.05). CONCLUSION For massive irreparable rotator cuff tears, arthroscopic superior capsular reconstruction using the "sandwich" patches combined with PRP injection can restore stability of the shoulder joint, relieve pain, promote rotator cuff healing, and achieve good short-term effectiveness.
Humans ; Platelet-Rich Plasma ; Female ; Male ; Middle Aged ; Aged ; Rotator Cuff Injuries/therapy* ; Arthroscopy/methods* ; Adult ; Retrospective Studies ; Aged, 80 and over ; Treatment Outcome ; Plastic Surgery Procedures/methods* ; Rotator Cuff/surgery* ; Range of Motion, Articular ; Shoulder Joint/surgery*

With the increasing proportion of human papilloma virus(HPV)infection in the pathogenic factors of oro-pharyngeal cancer,a series of changes have occurred in the surgical treatment.While the treatment mode has been im-proved,there are still many problems,including the inconsistency between diagnosis and treatment modes,the lack of popularization of reconstruction technology,the imperfect post-treatment rehabilitation system,and the lack of effective preventive measures.Especially in terms of treatment mode for early oropharyngeal cancer,there is no unified conclu-sion whether it is surgery alone or radiotherapy alone,and whether robotic minimally invasive surgery has better func-tional protection than radiotherapy.For advanced oropharyngeal cancer,there is greater controversy over the treatment mode.It is still unclear whether to adopt a non-surgical treatment mode of synchronous chemoradiotherapy or induction chemotherapy combined with synchronous chemoradiotherapy,or a treatment mode of surgery combined with postopera-tive chemoradiotherapy.In order to standardize the surgical treatment of oropharyngeal cancer in China and clarify the indications for surgical treatment of oropharyngeal cancer,this expert consensus,based on the characteristics and treat-ment status of oropharyngeal cancer in China and combined with the international latest theories and practices,forms consensus opinions in multiple aspects of preoperative evaluation,surgical indication determination,primary tumor re-section,neck lymph node dissection,postoperative defect repair,postoperative complication management prognosis and follow-up of oropharyngeal cancer patients.The key points include:① Before the treatment of oropharyngeal cancer,the expression of P16 protein should be detected to clarify HPV status;② Perform enhanced magnetic resonance imaging of the maxillofacial region before surgery to evaluate the invasion of oropharyngeal cancer and guide precise surgical resec-tion of oropharyngeal cancer.Evaluating mouth opening and airway status is crucial for surgical approach decisions and postoperative risk prediction;③ For oropharyngeal cancer patients who have to undergo major surgery and cannot eat for one to two months,it is recommended to undergo percutaneous endoscopic gastrostomy before surgery to effectively improve their nutritional intake during treatment;④ Early-stage oropharyngeal cancer patients may opt for either sur-gery alone or radiation therapy alone.For intermediate and advanced stages,HPV-related oropharyngeal cancer general-ly prioritizes radiation therapy,with concurrent chemotherapy considered based on tumor staging.Surgical treatment is recommended as the first choice for HPV unrelated oropharyngeal squamous cell carcinoma(including primary and re-current)and recurrent HPV related oropharyngeal squamous cell carcinoma after radiotherapy and chemotherapy;⑤ For primary exogenous T1-2 oropharyngeal cancer,direct surgery through the oral approach or da Vinci robotic sur-gery is preferred.For T3-4 patients with advanced oropharyngeal cancer,it is recommended to use temporary mandibu-lectomy approach and lateral pharyngotomy approach for surgery as appropriate;⑥ For cT1-2N0 oropharyngeal cancer patients with tumor invasion depth＞3 mm and cT3-4N0 HPV unrelated oropharyngeal cancer patients,selective neck dissection of levels ⅠB to Ⅳ is recommended.For cN+HPV unrelated oropharyngeal cancer patients,therapeutic neck dissection in regions Ⅰ-Ⅴ is advised;⑦ If PET-CT scan at 12 or more weeks after completion of radiation shows intense FDG uptake in any node,or imaging suggests continuous enlargement of lymph nodes,the patient should undergo neck dissection;⑧ For patients with suspected extracapsular invasion preoperatively,lymph node dissection should include removal of surrounding muscle and adipose connective tissue;⑨ The reconstruction of oropharyngeal cancer defects should follow the principle of reconstruction steps,with priority given to adjacent flaps,followed by distal pedicled flaps,and finally free flaps.The anterolateral thigh flap with abundant tissue can be used as the preferred flap for large-scale postoperative defects.

Objective:To investigate the predictive value of a radiomics model based on biparametric magnetic resonance imaging(bpMRI)for biochemical recurrence(BCR)after radical prostatectomy(RP)in elderly prostate cancer patients(≥60 years old).Methods:A retrospective analysis was conducted on data from 175 patients treated at Beijing Hospital from August 2017 to December 2021.Based on pathological results, image segmentation was performed on preoperative bpMRI T2, diffusion weighted imaging(DWI), and apparent diffusion coefficient(ADC)sequences.Pyradiomics was utilized to extract radiomic features, and Cox regression, Spearman correlation coefficient, and LASSO regression were employed for feature dimensionality reduction, leading to the construction of radiomic labels.Clinical models and image-clinical combined models were developed using multifactorial Cox regression analysis, and the performance of these models in predicting BCR was evaluated using the concordance index(C-index).Results:The 175 patients were randomly divided into a training set(122 cases)and a test set(53 cases)at a ratio of 7∶3, with 24 cases(19.7%, 24/122)and 11 cases(20.8%, 11/53)experiencing BCR, respectively.A total of 5 775 radiomic features were extracted from the three sequences, and after dimensionality reduction, 5 features were selected to construct the radiomic labels.The radiomics model exhibited C-index values of 0.764(95% CI: 0.655-0.872)and 0.769(95% CI: 0.632-0.906)in the training and test sets, respectively.Multifactorial Cox regression analysis revealed serum prostate-specific antigen(PSA)( HR=1.032, 95% CI: 1.010-1.054), postoperative pathology International Society of Urological Pathology(ISUP)grade grouping( HR=1.682, 95% CI: 1.039-2.722), and positive surgical margins( HR=2.513, 95% CI: 1.094-5.774)as independent predictors of BCR.The clinical model exhibited C-index values of 0.751(95% CI: 0.655-0.846)and 0.753(95% CI: 0.630-0.877)in the training and test sets, respectively.Following combined modeling of clinical factors and radiomic labels, the image-clinical combined model demonstrated the highest C-index values, namely 0.782(95% CI: 0.679-0.874)and 0.801(95% CI: 0.677-0.915)in the training and test sets, respectively. Conclusions:The radiomics model based on bpMRI can predict the occurrence of BCR after RP in elderly prostate cancer patients.Combined modeling of clinical factors and radiomic labels can enhance predictive efficiency.

Amantadine(AMD)residue can accumulate in organisms through the food chain and cause serious harm to human body.AMD can specifically bind to AMD specific aptamer and cause its conformation to change from a random single strand to a stem-loop structure.To avoid the influence of excess nucleotides on binding of aptamer to AMD,the truncation of the AMD original aptamer J was optimized by retaining an appropriate stem-loop structure,and a new type of truncation aptamers was developed in this work.By comparing the truncated aptamer with the original aptamer,it was found that the truncated aptamer J-7 had better affinity and specificity with AMD.The detection limit of AMD was 0.11 ng/mL by using J-7 as specific recognition element and molybdenum disulfide nanosheet(MoS2Ns)as signal amplification element.The developed method base on truncated aptamer J-7 was used for detection of AMD in milk,yogurt and SD rat serum samples for the first time with recoveries of 86.6%-108.2%.This study provided a reference for truncating other long sequence aptamers and provided a more sensitive detection method for monitoring AMD residues in food.