Low grade papillary transitional cell carcinoma pelvic recurrence masquerading as high grade invasive carcinoma, ten years after radical cystectomy

BACKGROUND: Tumor recurrence following radical cystectomy for a low-grade superficial transitional cell carcinoma (TCC) is exceedingly uncommon and has not been reported previously. CASE PRESENTATION: We describe a case of a young male presenting with anorexia, weight loss and a large, painful locally destructive pelvic recurrence, ten years after radical cystoprostatectomy. The pathology was consistent with a low-grade urothelial carcinoma. After an unsuccessful treatment with cisplatin-based chemotherapy, the patient underwent a curative intent hemipelvectomy with complete excision of tumor and is disease free at one year follow-up. CONCLUSION: A literature review related to this unusual presentation is reported and a surgical solutions over chemotherapy and radiotherapy is proposed

A clinical prediction model based on interpretable machine learning algorithms for prolonged hospital stay in acute ischemic stroke patients: a real-world study

ObjectiveAcute ischemic stroke (AIS) brings an increasingly heavier economic burden nowadays. Prolonged length of stay (LOS) is a vital factor in healthcare expenditures. The aim of this study was to predict prolonged LOS in AIS patients based on an interpretable machine learning algorithm.MethodsWe enrolled AIS patients in our hospital from August 2017 to July 2019, and divided them into the “prolonged LOS” group and the “no prolonged LOS” group. Prolonged LOS was defined as hospitalization for more than 7 days. The least absolute shrinkage and selection operator (LASSO) regression was applied to reduce the dimensionality of the data. We compared the predictive capacity of extended LOS in eight different machine learning algorithms. SHapley Additive exPlanations (SHAP) values were used to interpret the outcome, and the most optimal model was assessed by discrimination, calibration, and clinical utility.ResultsProlonged LOS developed in 149 (22.0%) of the 677 eligible patients. In eight machine learning algorithms, prolonged LOS was best predicted by the Gaussian naive Bayes (GNB) model, which had a striking area under the curve (AUC) of 0.878 ± 0.007 in the training set and 0.857 ± 0.039 in the validation set. The variables sorted by the gap values showed that the strongest predictors were pneumonia, dysphagia, thrombectomy, and stroke severity. High net benefits were observed at 0%–76% threshold probabilities, while good agreement was found between the observed and predicted probabilities.ConclusionsThe model using the GNB algorithm proved excellent for predicting prolonged LOS in AIS patients. This simple model of prolonged hospitalization could help adjust policies and better utilize resources

Immobilizing lead and copper in aqueous solution using microbial- and enzyme-induced carbonate precipitation

Inappropriate irrigation could trigger migration of heavy metals into surrounding environments, causing their accumulation and a serious threat to human central nervous system. Traditional site remediation technologies are criticized because they are time-consuming and featured with high risk of secondary pollution. In the past few years, the microbial-induced carbonate precipitation (MICP) is considered as an alternative to traditional technologies due to its easy maneuverability. The enzyme-induced carbonate precipitate (EICP) has attracted attention because bacterial cultivation is not required prior to catalyzing urea hydrolysis. This study compared the performance of lead (Pb) and copper (Cu) remediation using MICP and EICP respectively. The effect of the degree of urea hydrolysis, mass and species of carbonate precipitation, and chemical and thermodynamic properties of carbonates on the remediation efficiency was investigated. Results indicated that ammonium ion (NH4+) concentration reduced with the increase in lead ion (Pb2+) or copper ion (Cu2+) concentration, and for a given Pb2+ or Cu2+ concentration, it was much higher under MICP than EICP. Further, the remediation efficiency against Cu2+ is approximately zero, which is way below that against Pb2+ (approximately 100%). The Cu2+ toxicity denatured and even inactivated the urease, reducing the degree of urea hydrolysis and the remediation efficiency. Moreover, the reduction in the remediation efficiency against Pb2+ and Cu2+ appeared to be due to the precipitations of cotunnite and atacamite respectively. Their chemical and thermodynamic properties were not as good as calcite, cerussite, phosgenite, and malachite. The findings shed light on the underlying mechanism affecting the remediation efficiency against Pb2+ and Cu2+

Comparative efficacy of face-to-face and internet-based cognitive behavior therapy for generalized anxiety disorder: A meta-analysis of randomized controlled trial

ObjectiveThe study aimed to ascertain the comparative efficacy of these two forms on reducing anxiety scores of scales in patients with a generalized anxiety disorder (GAD) by examining the available evidence for face-to-face cognitive behavior therapy (CBT) and internet-based cognitive behavior therapy (ICBT). Moreover, this study attempted to determine whether ICBT can obtain similar benefits as CBT for GAD patients during coronavirus disease 2019 (COVID-19) due to the quarantine policy and the requirement of social distance.MethodsThis meta-analysis was registered with the International Prospective Register of Systematic Reviews (PROSPERO) according to the guidelines in the Preferred Reporting Items for Systematic Reviews and Meta-analyses statement (registration number CRD42021241938). Therefore, a meta-analysis of randomized controlled trials (RCTs) examining CBT or ICBT was conducted in this study to treat GAD patients diagnosed with DMS-IV. The researchers searched PubMed, MEDLINE, Embase, PsycINFO, and the Cochrane Database of Systematic Reviews for relevant studies published from 2000 to July 5, 2022. Evidence from RCTs was synthesized by Review Manager 5.4 as mean difference (MD) for change in scores of scales through a random-effects meta-analysis.ResultsA total of 26 trials representing 1,687 participants were pooled. The results demonstrated that ICBT and CBT were very close in the effect size of treating GAD (MD = −2.35 vs. MD = −2.79). Moreover, they still exhibited a similar response (MD = −3.45 vs. MD = −2.91) after studies with active control were removed.ConclusionRegarding the treatment of GAD, ICBT can achieve a similar therapeutic effect as CBT and could be CBT's candidate substitute, especially in the COVID-19 pandemic era, since the internet plays a crucial role in handling social space constraints.Systematic Review Registrationhttps://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=241938, identifier CRD42021241938

Myeloid sarcomas: a histologic, immunohistochemical, and cytogenetic study

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A machine learning model for visualization and dynamic clinical prediction of stroke recurrence in acute ischemic stroke patients: A real-world retrospective study

Background and purposeRecurrent stroke accounts for 25–30% of all preventable strokes, and this study was conducted to establish a machine learning-based clinical predictive rice idol for predicting stroke recurrence within 1 year in patients with acute ischemic stroke (AIS).MethodsA total of 645 AIS patients at The Second Affiliated Hospital of Xuzhou Medical University were screened, included and followed up for 1 year for comprehensive clinical data. Univariate and multivariate logistic regression (LR) were used to screen the risk factors of stroke recurrence. The data set was randomly divided into training set and test set according to the ratio of 7:3, and the following six prediction models were established by machine algorithm: random forest (RF), Naive Bayes model (NBC), decision tree (DT), extreme gradient boosting (XGB), gradient boosting machine (GBM) and LR. The model with the strongest prediction performance was selected by 10-fold cross-validation and receiver operating characteristic (ROC) curves, and the models were investigated for interpretability by SHAP. Finally, the models were constructed to be visualized using a web calculator.ResultsLogistic regression analysis showed that right hemisphere, homocysteine (HCY), C-reactive protein (CRP), and stroke severity (SS) were independent risk factors for the development of stroke recurrence in AIS patients. In 10-fold cross-validation, area under curve (AUC) ranked from 0.777 to 0.959. In ROC curve analysis, AUC ranged from 0.887 to 0.946. RF model has the best ability to predict stroke recurrence, and HCY has the largest contribution to the model. A web-based calculator https://mlmedicine-re-stroke2-re-stroke2-baylee.streamlitapp.com/ has been developed accordingly.ConclusionThis study identified four independent risk factors affecting recurrence within 1 year in stroke patients, and the constructed RF-based prediction model had good performance