A Wearable Electrochemical Biosensor Utilizing Functionalized Ti3C2Tx MXene for the Real-Time Monitoring of Uric Acid Metabolite.

Wearable, noninvasive sensors enable the continuous monitoring of metabolites in sweat and provide clinical information related to an individual's health and disease states. Uric acid (UA) is a key indicator highly associated with gout, hyperuricaemia, hypertension, kidney disease, and Lesch-Nyhan syndrome. However, the detection of UA levels typically relies on invasive blood tests. Therefore, developing a wearable device for noninvasive monitoring of UA concentrations in sweat could facilitate real-time personalized disease prevention. Here, we introduce 1,3,6,8-pyrene tetrasulfonic acid sodium salt (PyTS) as a bifunctional molecule functionalized with Ti3C2Tx via π-π conjugation to design nonenzymatic wearable sensors for sensitive and selective detection of UA concentration in human sweat. PyTS@Ti3C2Tx provides many oxidation-reduction active groups to enhance the electrocatalytic ability of the UA oxidation reaction. The PyTS@Ti3C2Tx-based electrochemical sensor demonstrates highly sensitive detection of UA in the concentration range of 5 µM-100 µM, exhibiting a lower detection limit of 0.48 µM compared to the uricase-based sensor (0.84 µM). In volunteers, the PyTS@Ti3C2Tx-based wearable sensor is integrated with flexible microfluidic sweat sampling and wireless electronics to enable real-time monitoring of UA levels during aerobic exercise. Simultaneously, it allows for comparison of blood UA levels via a commercial UA analyzer. Herein, this study provides a promising electrocatalyst strategy for nonenzymatic electrochemical UA sensor, enabling noninvasive real-time monitoring of UA levels in human sweat and personalized disease prevention.

Survival, treatment pattern, and treatment outcome in patients with cervical cancer metastatic to distant lymph nodes.

Background: Cervical cancer with nodal involvement beyond the pelvis was considered as distant nodal metastasis in the previous International Federation of Gynecology and Obstetrics staging system. With the improvement of cancer-directed therapies, some of these patients can receive curative treatment. Classifying them as distant metastasis may result in underestimation of their prognosis as well as undertreatment. However, limited research has been conducted on the survival and treatment pattern in distant lymphatic metastatic cervical cancer. Objective: To investigate the survival, treatment pattern, and treatment outcome of patients with cervical cancer metastasized to distant lymph nodes (DLN) beyond the pelvis. Methods: Patients with stage III-IV cervical cancer from 1988 to 2016 were identified using the Surveillance, Epidemiology, and End Results program. The cancer cause-specific survival (CSS) was analyzed using the Kaplan-Meier method, log-rank test, multivariable Cox proportional hazard regression, subgroup analysis, and propensity score-matched analysis. Results: Of 17783 patients with stage III-IV cervical cancer, patients with distant nodal disease beyond the pelvis (n=1883; included para-aortic lymph nodes metastasis) had superior survival compared to those with pelvic organ invasion or with distant organ(s) metastasis (5-year CSS, 32.3%, 26.3%, and 11.5%, respectively; adjusted P<0.001). The T stage significantly affected the survival of patients with positive DLN (5-year CSS for T1, T2, and T3: 47.3%, 37.0%, and 19.8%, respectively, adjusted P<0.01). For patients with positive DLN, combination radiotherapy (external beam radiotherapy [EBRT] with brachytherapy) prolonged CSS compared to EBRT alone (5-year CSS, 38.0% vs 21.7%; propensity score-adjusted HR, 0.60; 95% CI 0.51-0.72; P<0.001). Despite the superiority of combination radiotherapy, EBRT was the most frequently used treatment after 2004 (483/1214, 39.8%), while the utilization of combination radiotherapy declined from 37.8% (253/669) during 1988 through 2003 to 25.2% (306/1214) during 2004 through 2016. Conclusion: Patients with cervical cancer metastasized to DLN have favorable survival compared to those with pelvic organ invasion or with distant organ(s) metastasis. Their prognosis is significantly affected by local tumor burden and local treatment. Adequate and aggressive local radiotherapy, such as image-guided brachytherapy, can be considered for these patients to achieve better outcomes.

Superhydrophobic Functionalized Ti3C2Tx MXene-Based Skin-Attachable and Wearable Electrochemical pH Sensor for Real-Time Sweat Detection.

Sweat pH is a critical indicator for evaluating human health. With the extensive attention on the wearable and flexible biosensing devices, the technology for the monitoring of human sweat can be realized. In this study, a sensitive, miniaturized, and flexible electrochemical sweat pH sensor was developed for the continuous and real-time monitoring of the hydrogen-ion concentration in human sweat. A flexible electrode was fabricated on the poly(ethylene terephthalate) (PET) substrate by a simple and low-cost screen-printing technology, which was based on the integration of fluoroalkyl silane-functionalized Ti3C2Tx (F-Ti3C2Tx) and the polyaniline (PANI) membrane technology instead of the traditional ion-sensitive membrane. The surface functionalization strategy for Ti3C2Tx with perfluorodecyltrichlorosilane can provide environmental stability. Functionalized Ti3C2Tx (F-Ti3C2Tx) was doped with PANI to obtain improved responsiveness, sensitivity, and reversibility. The constructed microsize, portable, and wearable F-Ti3C2Tx/PANI pH sensor aimed to real-time monitor the pH value of human sweat during exercise. On-body sweat pH monitoring for females and males, respectively, exhibited high accuracy and continuous stability compared with ex situ analyses. This study thus offers a facile and practical solution for developing a highly reliable MXene-based mini-type pH sensor to realize the online monitoring of human sweat pH.

Proposing a deep learning-based method for improving the diagnostic certainty of pulmonary nodules in CT scan of chest.

OBJECTIVE: To compare the performance of a deep learning (DL)-based method for diagnosing pulmonary nodules compared with radiologists' diagnostic approach in computed tomography (CT) of the chest. MATERIALS AND METHODS: A total of 150 pathologically confirmed pulmonary nodules (60% malignant) assessed and reported by radiologists were included. CT images were processed by the proposed DL-based method to generate the probability of malignancy (0-100%), and the nodules were divided into the groups of benign (0-39.9%), indeterminate (40.0-59.9%), and malignant (60.0-100%). Taking the pathological results as the gold standard, we compared the diagnostic performance of the proposed DL-based method with the radiologists' diagnostic approach using the McNemar-Bowker test. RESULTS: There was a statistically significant difference between the diagnosis results of the proposed DL-based method and the radiologists' diagnostic approach (p < 0.001). Moreover, there was no statistically significant difference in the composition of the diagnosis results between the proposed DL-based method and the radiologists' diagnostic approach (all p > 0.05). The difference in diagnostic accuracy between the proposed DL-based method (70%) and radiologists' diagnostic performance (64%) was not statistically significant (p = 0.243). CONCLUSIONS: The proposed DL-based method achieved an accuracy comparable with the radiologists' diagnostic approach in clinical practice. Furthermore, its advantage in improving diagnostic certainty may raise the radiologists' confidence in diagnosing pulmonary nodules and may help clinical management. Therefore, the proposed DL-based method showed great potential in a certain clinical application. KEY POINTS: • Deep learning-based method for diagnosing the pulmonary nodules in computed tomography provides a higher diagnostic certainty.

Development and validation of a clinically applicable deep learning strategy (HONORS) for pulmonary nodule classification at CT: A retrospective multicentre study.

PURPOSE: To propose a practical strategy for the clinical application of deep learning algorithm, i.e., Hierarchical-Ordered Network-ORiented Strategy (HONORS), and a new approach to pulmonary nodule classification in various clinical scenarios, i.e., Filter-Guided Pyramid NETwork (FGP-NET). MATERIALS AND METHODS: We developed and validated FGP-NET on a collection of 2106 pulmonary nodules on computed tomography images which combined screened and clinically detected nodules, and performed external test (n = 341). The area under the curves (AUCs) of FGP-NET were assessed. A comparison study with a group of 126 skilled radiologists was conducted. On top of FGP-NET, we built up our HONORS which was composed of two solutions. In the Human Free Solution, we used the high sensitivity operating point for screened nodules, but the high specificity operating point for clinically detected nodules. In the Human-Machine Coupling Solution, we used the Youden point. RESULTS: FGP-NET achieved AUCs of 0.969 and 0.847 for internal and external test. The AUCs of the subsets of the external test set ranged from 0.890 to 0.942. The average sensitivity and specificity of the 126 radiologists were 72.2 ±â€¯15.1 % and 71.7 ±â€¯15.5 %, respectively, while a higher sensitivity (93.3 %) but a relatively inferior specificity (64.0 %) were achieved by FGP-NET. HONORS-guided FGP-NET identified benign nodules with high sensitivity (sensitivity,95.5 %; specificity, 72.5 %) in the screened nodules, and identified malignant nodules with high specificity (sensitivity, 31.0 %; specificity, 97.5 %) in the clinically detected nodules. These nodules could be reliably diagnosed without any intervention from radiologists, via the Human Free Solution. The remaining ambiguous nodules were diagnosed with high performance, which however required manual confirmation by radiologists, via the Human-Machine Coupling Solution. CONCLUSIONS: FGP-NET performed comparably to skilled radiologists in terms of diagnosing pulmonary nodules. HONORS, due to its high performance, might reliably contribute a second opinion, aiding in optimizing the clinical workflow.

Prognostic value of different metastatic sites for patients with FIGO stage IVB endometrial cancer after surgery: A SEER database analysis.

OBJECTIVE: This study aimed to investigate the association between different metastatic sites and survival in endometrial cancer (EC) patients with International Federation of Gynecology and Obstetrics (FIGO) stage IVB disease. METHODS: FIGO stage IVB patients with EC were selected from the surveillance, epidemiology, and end results database. Overall survival (OS) and cause-specific survival (CSS) were analyzed with Kaplan-Meier analysis and log-rank tests. Univariate and multivariate Cox proportional hazard models were used to identify the prognostic factors for OS and CSS. RESULTS: A total of 929 FIGO stage IVB patients with EC were identified. Patients with peritoneum metastasis were associated with significantly better OS and CSS compared to those with organ-specific metastasis (median OS: 29 vs 19 months, P = .005; median CSS: 47 vs 25 months, P < .001). Moreover, the survival superiority of peritoneum metastasis remained significant when organ-specific metastasis was further classified into specific single-organ metastasis. The multivariate analysis also indicated that compared with peritoneum metastasis, bone, brain, and lung metastasis were independent prognostic factors for worse OS. Similarly, distant lymph node, bone, brain, liver, and lung metastasis were associated with worse CSS. CONCLUSION: Metastatic sites affected prognosis in FIGO stage IVB patients with EC. Patients with peritoneum metastasis had significantly better survival outcomes than those with organ-specific metastasis.

Comprehensive lymphadenectomy and survival prediction in uterine serous cancer patients after surgery: A population-based analysis.

OBJECTIVE: Evidence on uterine serous cancer (USC) prognosis has been limited and inconclusive. We aim to explore the survival benefits of comprehensive lymphadenectomy in USC patients after surgery and develop a prognostic nomogram to predict survival. METHODS: USC patients who had undergone hysterectomy between 2010 and 2015 were identified from Surveillance, Epidemiology and End Results (SEER) database. The relationship between the extent of lymphadenectomy and survival, including overall survival (OS) and cancer-specific survival (CSS), was estimated with Kaplan-Meier (K-M) analysis. Univariate and multivariate Cox regression analyses were utilized to determine the independent prognostic factors. A nomogram was then developed, calibrated and internally validated. RESULTS: A total of 2853 patients were identified. K-M survival analysis revealed that patients with ≥12 pelvic lymph nodes (PLNs) removed had significantly better OS and CSS than those without (both P < 0.001). However, patients with ≥6 para-aortic lymph nodes removed was not associated with similar survival benefits than patients without (P > 0.1). Multivariate analyses for OS and CSS revealed that age, T-stage, N-stage, tumor size, adjuvant therapy and ≥12 PLNs removed were independent prognostic factors (all P < 0.05) and were subsequently incorporated into the nomogram. The Harrell's C-index of the nomogram was significantly higher than that of the FIGO staging system (OS: 0.739 vs 0.671, P < 0.001; CSS: 0.752 vs 0.695, P < 0.001). Furthermore, the nomogram was well calibrated with satisfactory consistency. CONCLUSIONS: Comprehensive pelvic lymphadenectomy should be recommended to USC patients for its survival benefits. And a nomogram has been developed to predict the survivals of USC patients after surgery.