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Contrast-induced nephropathy (CIN) represents a significant complication associated with the use of iodinated contrast media (ICM), especially in individuals with preexisting renal impairment. The pathophysiology of CIN encompasses oxidative stress, inflammation, endothelial dysfunction, and hemodynamic disturbances, resulting in acute kidney injury (AKI). Early detection is essential for effective management; however, conventional markers like serum creatinine (sCr) and estimated glomerular filtration rate (eGFR) exhibit limitations in sensitivity and timeliness. This review emphasizes the increasing significance of novel biomarkers in enhancing early detection and risk stratification of contrast-induced nephropathy (CIN). Recent advancements in artificial intelligence and computational analytics have improved the predictive capabilities of these biomarkers, enabling personalized risk assessment and precision medicine strategies. Additionally, we discuss mitigation strategies, including hydration protocols, pharmacological interventions, and procedural modifications, aimed at reducing CIN incidence. Incorporating biomarker-driven assessments into clinical decision-making can enhance patient management and outcomes. Future research must prioritize the standardization of biomarker assays, the validation of predictive models across diverse patient populations, and the exploration of novel therapeutic targets. Utilizing advancements in biomarkers and risk mitigation strategies allows clinicians to improve the safety of contrast-enhanced imaging and reduce the likelihood of renal injury.

Background Early identification of frail patients and early interventional treatment can minimize the frailty-related medical burden. This study investigated the use of machine learning (ML) to detect frailty in hospitalized older adults with acute illnesses. Methods We enrolled inpatients of the geriatric medicine ward at Taichung veterans general hospital between 2012 and 2022. We compared four ML models including logistic regression, random forest (RF), extreme gradient boosting, and support vector machine (SVM) for the prediction of frailty. The feature window as well as the prediction window was set as half a year before admission. Furthermore, Shapley additive explanation plots and partial dependence plots were used to identify Fried’s frailty phenotype for interpreting the model across various levels including domain, feature, and individual aspects. Results We enrolled 3367 patients. Of these, 2843 were frail. We used 21 features to train the prediction model. Of the 4 tested algorithms, SVM yielded the highest AUROC, precision and F1-score (78.05%, 94.53% and 82.10%). Of the 21 features, age, gender, multimorbidity frailty index, triage, hemoglobin, neutrophil ratio, estimated glomerular filtration rate, blood urea nitrogen, and potassium were identified as more impactful due to their absolute values. Conclusions Our results demonstrated that some easily accessed parameters from the hospital clinical data system can be used to predict frailty in older hospitalized patients using supervised ML methods.

While the internet provides abundant information, it often leads to information overload of users when purchasing goods. Tripadvisor.com, despite having a date sorting function, struggles to effectively filter relevant comments to users and neglects that consumer preferences may change over time. Therefore, this study aims to develop a website with visual charts showing changes in sentiment over time in reviews. The goal is to determine if this website improves user efficiency compared to the original website, reducing search time and aiding decision-making. The chart generation process involves four stages: collecting and preprocessing comments, constructing a hotel feature dictionary, classifying sentences and computing sentiment scores, and embedding charts on the website. 36 Tripadvisor.com users participate in experiments to evaluate the impact of old and new interfaces on answer quantity and search time. The NASA.tlx scale is used to assess the mental load experienced with both interfaces.

Understanding the particle size distribution in the air and patterns of environmental contamination of SARS-CoV-2 is essential for infection prevention policies. Here we screen surface and air samples from hospital rooms of COVID-19 patients for SARS-CoV-2 RNA. Environmental sampling is conducted in three airborne infection isolation rooms (AIIRs) in the ICU and 27 AIIRs in the general ward. 245 surface samples are collected. 56.7% of rooms have at least one environmental surface contaminated. High touch surface contamination is shown in ten (66.7%) out of 15 patients in the first week of illness, and three (20%) beyond the first week of illness ( p  = 0.01, χ 2 test). Air sampling is performed in three of the 27 AIIRs in the general ward, and detects SARS-CoV-2 PCR-positive particles of sizes >4 µm and 1–4 µm in two rooms, despite these rooms having 12 air changes per hour. This warrants further study of the airborne transmission potential of SARS-CoV-2. Here, the authors sample air and surfaces in hospital rooms of COVID-19 patients, detect SARS-CoV-2 RNA in air samples of two of three tested airborne infection isolation rooms, and find surface contamination in 66.7% of tested rooms during the first week of illness and 20% beyond the first week of illness.

Purpose This paper is concerned with the design and analysis of a bearingless motor. Design/methodology/approach The bearingless motor is obtained by a regular three-pole active magnetic bearing with an intentionally attached unbalanced mass on the rotor. It is the unbalanced mass that will generate the rotational torque for the motor function. Modeling and control of the unbalanced mass-type bearingless motor have been considered. Findings It is found through simulations that both functions of motor and magnetic bearing can indeed be achieved in this system. Originality/value This novel bearingless motor requires no additional windings and permanent magnets. Thus, it can greatly reduce the cost and design of the bearingless motor.

The use of three-dimensional(3D) printing is broadly across many medical specialties. It is an innovative, and rapidly growing technology to produce custom anatomical models and medical conditions models for medical teaching, surgical planning, and patient education. This study aimed to evaluate the accuracy and feasibility of 3D printing in creating a superficial femoral artery pseudoaneurysm model based on CT scans for endovascular training. A case of a left superficial femoral artery pseudoaneurysm was selected, and the 3D model was created using DICOM files imported into Materialise Mimics 22.0 and Materialise 3-Matic software, then printed using vat polymerization technology. Two 3D-printed models were created, and a series of comparisons were conducted between the 3D segmented images from CT scans and these two 3D-printed models. Ten comparisons involving internal diameters and angles of the specific anatomical location were measured. The study found that the absolute mean difference in diameter between the 3D segmented images and the 3D printed models was 0.179±0.145 mm and 0.216±0.143mm, respectively, with no significant difference between the two sets of models. Additionally, the absolute mean difference in angle was 0.99±0.65° and 1.00±0.91°, respectively, and the absolute mean difference in angle between the two sets of data was not significant. Bland-Altman analysis confirmed a high correlation in dimension measurements between the 3D-printed models and segmented images. Furthermore, the accuracy of a 3D-printed femoral pseudoaneurysm model was further tested through the simulation of a superficial femoral artery pseudoaneurysm coiling procedure using the Philips Azurion7 in the angiography room. 3D printing is a reliable technique for producing a high accuracy 3D anatomical model that closely resemble a patient's anatomy based on CT images. Additionally, 3D printing is a feasible and viable option for use in endovascular training and medical education. In general, 3D printing is an encouraging technology with diverse possibilities in medicine, including surgical planning, medical education, and medical device advancement.

Background The accuracy of estimating microvascular invasion (MVI) preoperatively in hepatocellular carcinoma (HCC) by clinical observers is low. Most recent studies constructed MVI predictive models utilizing radiological and/or radiomics features extracted from computed tomography (CT) images. These methods, however, rely heavily on human experiences and require manual tumor contouring. We developed a deep learning-based framework for preoperative MVI prediction by using CT images of arterial phase (AP) with simple tumor labeling and without the need of manual feature extraction. The model was further validated on CT images that were originally scanned at multiple different hospitals. Methods CT images of AP were acquired for 309 patients from China Medical University Hospital (CMUH). Images of 164 patients, who took their CT scanning at 54 different hospitals but were referred to CMUH, were also collected. Deep learning (ResNet-18) and machine learning (support vector machine) models were constructed with AP images and/or patients’ clinical factors (CFs), and their performance was compared systematically. All models were independently evaluated on two patient cohorts: validation set (within CMUH) and external set (other hospitals). Subsequently, explainability of the best model was visualized using gradient-weighted class activation map (Grad-CAM). Results The ResNet-18 model built with AP images and patients’ clinical factors was superior than other models achieving a highest AUC of 0.845. When evaluating on the external set, the model produced an AUC of 0.777, approaching its performance on the validation set. Model interpretation with Grad-CAM revealed that MVI relevant imaging features on CT images were captured and learned by the ResNet-18 model. Conclusions This framework provide evidence showing the generalizability and robustness of ResNet-18 in predicting MVI using CT images of AP scanned at multiple different hospitals. Attention heatmaps obtained from model explainability further confirmed that ResNet-18 focused on imaging features on CT overlapping with the conditions used by radiologists to estimate MVI clinically.

Data on use of monoclonal antibodies (mAbs) in hospitalized patients are limited. In this cross-sectional study, we evaluated the use of mAbs for early treatment of unvaccinated hospitalized patients with mild-to-moderate COVID-19. All inpatients at our center were screened on 27 October 2021. Primary outcome was in-hospital deterioration as defined by a composite of oxygen requirement, intensive care unit (ICU) admission, or mortality within 28 days of admission. Ninety-four out of 410 COVID-19 inpatients were included in the final analysis, of whom 19 (20.2%) received early treatment with sotrovimab. The median age was 73 years (IQR 61–83), and 35 (37.2%) were female. Although the treatment group was significantly older and had more comorbidities, there was a lower proportion of progression to oxygen requirement (31.6% vs. 54.7%), ICU admission (10.5% vs. 24.0%), or mortality (5.3% vs. 13.3%). Kaplan–Meier curves showed a significant difference in time to in-hospital deterioration (log-rank test, p = 0.043). Cox proportional hazards model for in-hospital deterioration showed that sotrovimab treatment was protective (hazard ratio, 0.41; 95% CI, 0.17–0.99; p = 0.047) after adjustment for baseline ISARIC deterioration score. Our findings support the use of sotrovimab for early treatment in hospitalized patients with mild-to-moderate COVID-19 at a high risk of disease progression.

Abstract Background Prolonged fever is associated with adverse outcomes in dengue viral infection. Similar fever patterns are observed in COVID-19 with unclear significance. Methods We conducted a hospital-based case–control study of patients admitted for COVID-19 with prolonged fever (fever >7 days) and saddleback fever (recurrence of fever, lasting <24 hours, after defervescence beyond day 7 of illness). Fever was defined as a temperature of ≥38.0°C. Cytokines were determined with multiplex microbead-based immunoassay for a subgroup of patients. Adverse outcomes were hypoxia, intensive care unit (ICU) admission, mechanical ventilation, and mortality. Results A total of 142 patients were included in the study; 12.7% (18/142) of cases had prolonged fever, and 9.9% (14/142) had saddleback fever. Those with prolonged fever had a median duration of fever (interquartile range [IQR]) of 10 (9–11) days for prolonged fever cases, while fever recurred at a median (IQR) of 10 (8–12) days for those with saddleback fever. Both prolonged (27.8% vs 0.9%; P < .01) and saddleback fever (14.3% vs 0.9%; P = .03) were associated with hypoxia compared with controls. Cases with prolonged fever were also more likely to require ICU admission compared with controls (11.1% vs 0.9%; P = .05). Patients with prolonged fever had higher induced protein–10 and lower interleukin-1α levels compared with those with saddleback fever at the early acute phase of disease. Conclusions Prolonged fever beyond 7 days from onset of illness can identify patients who may be at risk of adverse outcomes from COVID-19. Patients with saddleback fever appeared to have good outcomes regardless of the fever.

Objectives A wide range of duration of viral RNA shedding in patients infected with Severe Acute Respiratory Syndrome Coronavirus 2 (SARS‐CoV‐2) has been observed. We aimed to investigate factors associated with prolonged and intermittent viral RNA shedding in a retrospective cohort of symptomatic COVID‐19 patients. Methods Demographic, clinical and laboratory data from hospitalised COVID‐19 patients from a single centre with two consecutive negative respiratory reverse transcription‐polymerase chain reaction (RT‐PCR) results were extracted from electronic medical records. Kaplan–Meier survival curve analysis was used to assess the effect of clinical characteristics on the duration and pattern of shedding. Plasma levels of immune mediators were measured using Luminex multiplex microbead‐based immunoassay. Results There were 201 symptomatic patients included. Median age was 49 years (interquartile range 16–61), and 52.2% were male. Median RNA shedding was 14 days (IQR 9–18). Intermittent shedding was observed in 77 (38.3%). We did not identify any factor associated with prolonged or intermittent viral RNA shedding. Duration of shedding was inversely correlated with plasma levels of T‐cell cytokines IL‐1β and IL‐17A at the initial phase of infection, and patients had lower levels of pro‐inflammatory cytokines during intermittent shedding. Conclusions Less active T‐cell responses at the initial phase of infection were associated with prolonged viral RNA shedding, suggesting that early immune responses are beneficial to control viral load and prevent viral RNA shedding. Intermittent shedding is common and may explain re‐detection of viral RNA in recovered patients. We studied 201 patients with PCR‐confirmed COVID‐19 infection. We found median RNA shedding was 14 days and intermittent RNA shedding was observed in 38.3%. The only associated clinical factor with prolonged RNA shedding was invasive mechanical ventilation. Importantly, we observed in a subset of patients with cytokine analysis, that prolonged RNA shedding was associated with EGF, FGF‐2, GRO‐α and RANTES at the initial phase of infection. Intermittent RNA shedding was associated with lower levels of pro‐inflammatory cytokines.