Biomimetic Hydrodynamic Sensor with Whisker Array Architecture and Multidirectional Perception Ability.

The rapid development of ocean exploration and underwater robot technology has put forward new requirements for underwater sensing methods, which can be used for hydrodynamic characteristics perception, underwater target tracking, and even underwater cluster communication. Here, inspired by the specialized undulated surface structure of the seal whisker and its ability to suppress vortex-induced vibration, a multidirectional hydrodynamic sensor based on biomimetic whisker array structure and magnetic 3D self-decoupling theory is introduced. The magnetic-based sensing method enables wireless connectivity between the magnetic functional structures and electronics, simplifying device design and endowing complete watertightness. The 3D self-decoupling capability enables the sensor, like a seal or other organisms, to perceive arbitrary whisker motions caused by the action of water flow without complex calibration and additional sensing units. The whisker sensor is capable of detecting a variety of hydrodynamic information, including the velocity (RMSE < 0.061 m s-1) and direction of the steady flow field, the frequency (error < 0.05 Hz) of the dynamic vortex wake, and the orientation (error < 7°) of the vortex wake source, demonstrating its extensive potential for underwater environmental perception and communication, especially in deep sea conditions.

Serum activin A as a prognostic biomarker for community acquired pneumonia.

BACKGROUND: It is essential to develop new biomarker with effective prognostic roles because of the unclear clinical use of the current community-acquired pneumonia (CAP) predictors. AIM: To evaluate the association between serum activin A levels and prognosis in CAP patients. METHODS: A total of 168 CAP individuals grouped according to the severity and prognosis of illness condition, and 48 healthy individuals as the control group were enrolled in this study. Circulating concentrations of activin A were measured using enzyme-linked immunoassays. The interaction between activin A levels and etiologies of CAP was determined. Based on the severity of CAP, 110 patients (65.48%) were categorized into group-I, 42 (25%) cases were grouped into group-II, and 16 (9.52%) cases were categorized into group-III. RESULTS: Serum activin A levels were higher in patients with CAP than controls, but independent of etiology. Moreover, the scores of Pneumonia Severity Index (PSI) and CURB-65 positively correlated with the increasing levels of serum activin A, and were at their highest peak in individuals in group-III (P < 0.001). Combining activin A with CURB-65 or PSI was more effective in improving predictive property (P < 0.01). According to Cox proportional regression analysis, after adjusting clinical parameters, we confirmed that activin A showed a powerful predictive property for hospital mortality in CAP patients (P < 0.001). CONCLUSION: Higher level of serum activin A was associated with poor prognosis of CAP. Activin A can be used as a more valuable biomarker of prognosis in CAP patients.

Validation of Noninvasive Detection of Hyperkalemia by Artificial Intelligence-Enhanced Electrocardiography in High Acuity Settings.

Background: Artificial intelligence (AI) electrocardiogram (ECG) analysis can enable detection of hyperkalemia. In this validation, we assessed the algorithm's performance in two high acuity settings. Methods: An emergency department (ED) cohort (February to August 2021) and a mixed intensive care unit (ICU) cohort (August 2017 to February 2018) were identified and analyzed separately. For each group, pairs of laboratory-collected potassium and 12 lead ECGs obtained within 4 hours of each other were identified. The previously developed AI ECG algorithm was subsequently applied to leads 1 and 2 of the 12 lead ECGs to screen for hyperkalemia (potassium >6.0 mEq/L). Results: The ED cohort (N=40,128) had a mean age of 60 years, 48% were male, and 1% (N=351) had hyperkalemia. The area under the curve (AUC) of the AI-enhanced ECG (AI-ECG) to detect hyperkalemia was 0.88, with sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and positive likelihood ratio (LR+) of 80%, 80%, 3%, 99.8%, and 4.0, respectively, in the ED cohort. Low-eGFR (<30 ml/min) subanalysis yielded AUC, sensitivity, specificity, PPV, NPV, and LR+ of 0.83, 86%, 60%, 15%, 98%, and 2.2, respectively, in the ED cohort. The ICU cohort (N=2636) had a mean age of 65 years, 60% were male, and 3% (N=87) had hyperkalemia. The AUC for the AI-ECG was 0.88 and yielded sensitivity, specificity, PPV, NPV, and LR+ of 82%, 82%, 14%, 99%, and 4.6, respectively in the ICU cohort. Low-eGFR subanalysis yielded AUC, sensitivity, specificity, PPV, NPV, and LR+ of 0.85, 88%, 67%, 29%, 97%, and 2.7, respectively in the ICU cohort. Conclusions: The AI-ECG algorithm demonstrated a high NPV, suggesting that it is useful for ruling out hyperkalemia, but a low PPV, suggesting that it is insufficient for treating hyperkalemia.

In-Hospital Adverse Events of Pheochromocytoma-Induced Takotsubo Syndrome: A Literature Review and Cluster Analysis of 172 Cases.

Background: Pheochromocytoma-induced takotsubo syndrome (Pheo-TTS) significantly increases the risk of adverse events for inpatient. The early identification of risk factors at admission is crucial for effective risk stratification and minimizing complications in Pheo-TTS patients. Methods: We conducted a systematic review combined with hierarchical cluster and feature importance analysis of demographic, clinical and laboratory data upon admission, alongside in-hospital complication data for Pheo-TTS patients. We analyzed cases published in PubMed and Embase from 2 May 2006 to 27 April 2023. Results: Among 172 Pheo-TTS patients, cluster analysis identified two distinct groups: a chest pain dominant (CPD) group (n = 86) and a non-chest pain dominant (non-CPD) group (n = 86). The non-CPD group was characterized by a younger age (44.0 ± 15.2 vs. 52.4 ± 14.4, p < 0.001), a higher prevalence of neurological/psychiatric disorders (53.5% vs. 32.6%), and increased presentation of dyspnea (87.2% vs. 17.4%), pulmonary rales (59.3% vs. 8.1%), and tachycardia (77.9% vs. 30.2%). Additionally, they exhibited more atypical takotsubo syndrome (TTS) imaging phenotypes (55.8% vs. 36.5%, all p < 0.05). The non-CPD group experienced more than a 2-fold increase for in-hospital adverse events compared to the CPD group (70.9% vs. 30.2%, p < 0.001). After adjusting for confounding factors, the absence of chest pain (odds ratio [OR] = 0.407, 95% confidence interval [CI] 0.169-0.979, p = 0.045), the presence of abdominal symptoms (OR = 3.939, 95% CI 1.770-8.766, p = 0.001), pulmonary rales (OR = 4.348, 95% CI 1.857-10.179, p = 0.001), and atypical TTS imaging phenotype (OR = 3.397, 95% CI 1.534-7.525, p = 0.003) remained as independent predictors of in-hospital complications. Conclusions: Clinical manifestations and imaging features at admission help to predict in-hospital complications for Pheo-TTS patients.

ROS-mediated thylakoid membrane remodeling and triacylglycerol biosynthesis under nitrogen starvation in the alga Chlorella sorokiniana.

Many microbes accumulate energy storage molecules such as triglycerides (TAG) and starch during nutrient limitation. In eukaryotic green algae grown under nitrogen-limiting conditions, triglyceride accumulation is coupled with chlorosis and growth arrest. In this study, we show that reactive oxygen species (ROS) actively accumulate during nitrogen limitation in the microalga Chlorella sorokiniana. Accumulation of ROS is mediated by the downregulation of genes encoding ROS-quenching enzymes, such as superoxide dismutases, catalase, peroxiredoxin, and glutathione peroxidase-like, and by the upregulation of enzymes involved in generating ROS, such as NADPH oxidase, xanthine oxidase, and amine oxidases. The expression of genes involved in ascorbate and glutathione metabolism is also affected under this condition. ROS accumulation contributes to the degradation of monogalactosyl diacylglycerol (MGDG) and thylakoid membrane remodeling, leading to chlorosis. Quenching ROS under nitrogen limitation reduces the degradation of MGDG and the accumulation of TAG. This work shows that ROS accumulation, membrane remodeling, and TAG accumulation under nitrogen limitation are intricately linked in the microalga C. sorokiniana.

Takotsubo Syndrome in Patients With COVID-19: A Systematic Review.

Background: Respiratory conditions are major physical triggers of takotsubo syndrome (TTS) and portend worse outcomes. However, data on TTS in patients with coronavirus disease-2019 infection (COVID-19) are limited. Methods: We searched PubMed, Embase, and Cochrane Library databases for case reports for the period 2019-2022 describing TTS in patients with COVID-19 pneumonia (TTS-COVID). We summarized the clinical data and outcomes and compared them to those in patients with TTS with an acute respiratory disease other than COVID-19 as a trigger (TTS-acute respiratory disease) and those with TTS with no respiratory disease (TTS-no respiratory disease). Results: The mortality rate was higher in those with TTS-COVID (26.0%) than those with TTS-acute respiratory disease (5.7%) or TTS-no respiratory disease (4.2%; P < 0.001 for both). The proportion of men was higher in TTS-COVID (33.3%) than it was in TTS-no respiratory disease (9.1%; P < 0.001). The manifestations of TTS in COVID patients were atypical (dyspnea [70.3%] and cough [40.6%]); few had chest pain (23.4%). Cardiovascular risk factors were common in the TTS-COVID cohort, but fewer patients were on cardioprotective medications in this group than in the other 2 groups. Level of catecholamine use was higher in the TTS-COVID group (37.7%) than it was in the TTS-no respiratory disease (10.9%; P < 0.001) group. Apical ballooning (72.6%) was the most common TTS subtype, and basal segment type was seen in 11.0% of TTS-COVID patients. Conclusions: COVID-19 patients who developed TTS had high mortality rates and unique features, compared with those in the TTS-acute respiratory disease group or the TTS-no respiratory disease group. Understanding the pathophysiology of TTS in COVID-19 may help prevent TTS and direct therapy in this setting.

Contexte: Les troubles respiratoires sont des déclencheurs physiques importants du syndrome de Takotsubo (STT) et présagent une issue funeste. Les données sur le STT chez les personnes ayant contracté la maladie à coronavirus de 2019 (COVID-19) sont néanmoins limitées. Méthodologie: Nous avons fait une recherche dans les bases de données PubMed, Embase et Cochrane Library pour trouver des rapports de cas signalés entre 2019 et 2022 faisant état du STT chez des patients ayant contracté une pneumonie associée à la COVID-19 (STT-COVID). Nous avons synthétisé les données cliniques et les résultats pour les comparer à ceux de patients atteints du STT déclenché par une autre maladie respiratoire aiguë que la COVID-19 (STT-maladie respiratoire aiguë) et de patients atteints du STT sans maladie respiratoire (STT-sans maladie respiratoire). Résultats: Le taux de mortalité a été plus élevé chez les patients atteints du STT-COVID (26,0 %) que chez ceux atteints du STT-maladie respiratoire aiguë (5,7 %) ou du STT-sans maladie respiratoire (4,2 %; p < 0,001 dans les deux cas). La proportion d'hommes était plus élevée dans le groupe STT-COVID (33,3 %) que dans le groupe STT-sans maladie respiratoire (9,1 %; p < 0,001). Les manifestations du STT chez les patients atteints de la COVID étaient atypiques (dyspnée [70,3 %] et toux [40,6 %]); quelques patients présentaient une douleur thoracique (23,4 %). Les facteurs de risque cardiovasculaires étaient courants dans la cohorte STT-COVID, mais les patients qui prenaient des médicaments cardioprotecteurs étaient moins nombreux dans ce groupe que dans les deux autres groupes. Le taux d'utilisation de la catécholamine était plus élevé dans le groupe STT-COVID (37,7 %) que dans le groupe STT-sans maladie respiratoire (10,9 %; p < 0,001). La ballonisation de l'apex (72,6 %) était le sous-type de STT le plus courant, et le type caractérisé par un trouble du segment basal a été observé chez 11,0 % des patients atteints du STT-COVID. Conclusions: Les patients atteints de la COVID-19 ayant développé un STT présentaient des taux de mortalité élevés et des manifestations singulières, comparativement à ceux du groupe STT-maladie respiratoire aiguë ou du groupe STT-sans maladie respiratoire. Comprendre la physiopathologie du STT chez les patients atteints de la COVID-19 pourrait contribuer à prévenir le STT et à orienter le traitement dans ce contexte.

Safety, Efficacy, and Pharmacokinetics of SHR-A1811, a Human Epidermal Growth Factor Receptor 2-Directed Antibody-Drug Conjugate, in Human Epidermal Growth Factor Receptor 2-Expressing or Mutated Advanced Solid Tumors: A Global Phase I Trial.

PURPOSE: SHR-A1811 is an antibody-drug conjugate composed of an anti-human epidermal growth factor receptor 2 (HER2) antibody trastuzumab, a cleavable linker, and a topoisomerase I inhibitor payload. We assessed the safety, tolerability, antitumor activity, and pharmacokinetics of SHR-A1811 in heavily pretreated HER2-expressing or mutated advanced solid tumors. METHODS: This global, multi-center, first-in-human, phase I trial was conducted at 33 centers. Patients who had HER2-expressing or mutated unresectable, advanced, or metastatic solid tumors and were refractory or intolerant to standard therapies were enrolled. SHR-A1811 was administered intravenously at doses ranging from 1.0 to 8.0 mg/kg once every 3 weeks. The primary end points were dose-limiting toxicity, safety, and the recommended phase II dose. RESULTS: From September 7, 2020, to February 27, 2023, 307 patients who had undergone a median of three (IQR, 2-5) previous treatment regimens in the metastatic setting received SHR-A1811 treatment. As of data cutoff (February 28, 2023), one patient from the 6.4 mg/kg group experienced dose-limiting toxicities (pancytopenia and colitis). The most common grade 3 or higher adverse events (AEs) included decreased neutrophil count (119 [38.8%]) and decreased WBC count (70 [22.8%]). Interstitial lung disease occurred in only eight (2.6%) patients. Serious AEs and deaths occurred in 70 (22.8%) and 13 (4.2%) patients, respectively. SHR-A1811 led to objective responses in 59.9% (184/307) of all patients, 76.3% (90/118) of HER2-positive breast cancer, 60.4% (55/91) of HER2 low-expressing breast cancer, and 45.9% (39/85 with evaluable tumor responses) of the 98 nonbreast tumors. CONCLUSION: SHR-A1811 exhibited acceptable tolerability, promising antitumor activity, and a favorable pharmacokinetic profile in heavily pretreated advanced solid tumors. The recommended phase II dose of 4.8 or 6.4 mg/kg was selected for various tumor types.

Real-Time Multi-Sensor Joint Fault Diagnosis Method for Permanent Magnet Traction Drive Systems Based on Structural Analysis.

Sensor faults are one of the most common faults that cause performance degradation or functional loss in permanent magnet traction drive systems (PMTDSs). To quickly diagnose faulty sensors, this paper proposes a real-time joint diagnosis method for multi-sensor faults based on structural analysis. Firstly, based on limited monitoring signals on board, a structured model of the system was established using the structural analysis method. The isolation and detectability of faulty sensors were analyzed using the Dulmage-Mendelsohn decomposition method. Secondly, the minimum collision set method was used to calculate the minimum overdetermined equation set, transforming the higher-order system model into multiple related subsystem models, thereby reducing modeling complexity and facilitating system implementation. Next, residual vectors were constructed based on multiple subsystem models, and fault detection and isolation strategies were designed using the correlation between each subsystem model and the relevant sensors. The validation results of the physical testing platform based on online fault data recordings showed that the proposed method could achieve rapid fault detection and the localization of multi-sensor faults in PMTDS and had a good application value.

A study on the mechanism of Beclin-1 m6A modification mediated by catalpol in protection against neuronal injury and autophagy following cerebral ischemia.

OBJECTIVE: Catalpol (CAT) has various pharmacological activities and plays a protective role in cerebral ischemia. It has been reported that CAT played a protective role in cerebral ischemia by upregulaing NRF1 expression. Bioinformatics analysis reveals that NRF1 can be used as a transcription factor to bind to the histone acetyltransferase KAT2A. However, the role of KAT2A in cerebral ischemia remains to be studied. Therefore, we aimed to investigate the role of CAT in cerebral ischemia and its related mechanism. METHODS: In vitro, a cell model of oxygen and glucose deprivation/reperfusion (OGD/R) was constructed, followed by evaluation of neuronal injury and the expression of METTL3, Beclin-1, NRF1, and KAT2A. In vivo, a MCAO rat model was prepared by means of focal cerebral ischemia, followed by assessment of neurological deficit and brain injury in MCAO rats. Neuronal autophagy was evaluated by observation of autophagosomes in neurons or brain tissues by TEM and detection of the expression of LC3 and p62. RESULTS: In vivo, CAT reduced the neurological function deficit and infarct volume, inhibited neuronal apoptosis in the cerebral cortex, and significantly improved neuronal injury and excessive autophagy in MCAO rats. In vitro, CAT restored OGD/R-inhibited cell viability, inhibited cell apoptosis, LDH release, and neuronal autophagy. Mechanistically, CAT upregulated NRF1, NRF1 activated METTL3 via KAT2A transcription, and METTL3 inhibited Beclin-1 via m6A modification. CONCLUSION: CAT activated the NRF1/KAT2A/METTL3 axis and downregulated Beclin-1 expression, thus relieving neuronal injury and excessive autophagy after cerebral ischemia.

Deep learning from latent spatiotemporal information of the heart: Identifying advanced bioimaging markers from echocardiograms.

A key strength of echocardiography lies in its integration of comprehensive spatiotemporal cardiac imaging data in real-time, to aid frontline or bedside patient risk stratification and management. Nonetheless, its acquisition, processing, and interpretation are known to all be subject to heterogeneity from its reliance on manual and subjective human tracings, which challenges workflow and protocol standardization and final interpretation accuracy. In the era of advanced computational power, utilization of machine learning algorithms for big data analytics in echocardiography promises reduction in cost, cognitive errors, and intra- and inter-observer variability. Novel spatiotemporal deep learning (DL) models allow the integration of temporal arm information based on unlabeled pixel echocardiographic data for convolution of an adaptive semantic spatiotemporal calibration to construct personalized 4D heart meshes, assess global and regional cardiac function, detect early valve pathology, and differentiate uncommon cardiovascular disorders. Meanwhile, data visualization on spatiotemporal DL prediction models helps extract latent temporal imaging features to develop advanced imaging biomarkers in early disease stages and advance our understanding of pathophysiology to support the development of personalized prevention or treatment strategies. Since portable echocardiograms have been increasingly used as point-of-care imaging tools to aid rural care delivery, the application of these new spatiotemporal DL techniques show the potentials in streamlining echocardiographic acquisition, processing, and data analysis to improve workflow standardization and efficiencies, and provide risk stratification and decision supporting tools in real-time, to prompt the building of new imaging diagnostic networks to enhance rural healthcare engagement.

Ultrasimple size encoded microfluidic chip for rapid simultaneous multiplex detection of DNA sequences.

Simultaneous multiplexed analysis can provide comprehensive information for disease diagnosis. However, the current multiplex methods rely on sophisticated barcode technology, which hinders its wider application. In this study, an ultrasimple size encoding method is proposed for multiplex detection using a wedge-shaped microfluidic chip. Driving by negative pressure, microparticles are naturally arranged in distinct stripes based on their sizes within the chip. This size encoding method demonstrates a high level of precision, allowing for accuracy in distinguishing 3-5 sizes of microparticles with a remarkable accuracy rate of up to 99%, even the microparticles with a size difference as small as 0.5 µm. The entire size encoding process is completed in less than 5 min, making it ultrasimple, reliable, and easy to operate. To evaluate the function of this size encoding microfluidic chip, three commonly co-infectious viruses' nucleic acid sequences (including complementary DNA sequences of HIV and HCV, and DNA sequence of HBV) are employed for multiplex detection. Results indicate that all three DNA sequences can be sensitively detected without any cross-interference. This size-encoding microfluidic chip-based multiplex detection method is simple, rapid, and high-resolution, its successful application in serum samples renders it highly promising for potential clinical promotion.

Artificial intelligence-enhanced electrocardiogram for arrhythmogenic right ventricular cardiomyopathy detection.

Aims: ECG abnormalities are often the first signs of arrhythmogenic right ventricular cardiomyopathy (ARVC) and we hypothesized that an artificial intelligence (AI)-enhanced ECG could help identify patients with ARVC and serve as a valuable disease-detection tool. Methods and results: We created a convolutional neural network to detect ARVC using a 12-lead ECG. All patients with ARVC who met the 2010 task force criteria and had disease-causative genetic variants were included. All case ECGs were randomly assigned in an 8:1:1 ratio into training, validation, and testing groups. The case ECGs were age- and sex-matched with control ECGs at our institution in a 1:100 ratio. Seventy-seven patients (51% male; mean age 47.2 ± 19.9), including 56 patients with PKP2, 7 with DSG2, 6 with DSC2, 6 with DSP, and 2 with JUP were included. The model was trained using 61 case ECGs and 5009 control ECGs; validated with 7 case ECGs and 678 control ECGs and tested in 22 case ECGs and 1256 control ECGs. The sensitivity, specificity, positive and negative predictive values of the model were 77.3, 62.9, 3.32, and 99.4%, respectively. The area under the curve for rhythm ECG and median beat ECG was 0.75 and 0.76, respectively. Conclusion: Our study found that the model performed well in excluding ARVC and supports the concept that the AI ECG can serve as a biomarker for ARVC if a larger cohort were available for network training. A multicentre study including patients with ARVC from other centres would be the next step in refining, testing, and validating this algorithm.

Deep learning from atrioventricular plane displacement in patients with Takotsubo syndrome: lighting up the black-box.

Aims: The spatiotemporal deep convolutional neural network (DCNN) helps reduce echocardiographic readers' erroneous 'judgement calls' on Takotsubo syndrome (TTS). The aim of this study was to improve the interpretability of the spatiotemporal DCNN to discover latent imaging features associated with causative TTS pathophysiology. Methods and results: We applied gradient-weighted class activation mapping analysis to visualize an established spatiotemporal DCNN based on the echocardiographic videos to differentiate TTS (150 patients) from anterior wall ST-segment elevation myocardial infarction (STEMI, 150 patients). Forty-eight human expert readers interpreted the same echocardiographic videos and prioritized the regions of interest on myocardium for the differentiation. Based on visualization results, we completed optical flow measurement, myocardial strain, and Doppler/tissue Doppler echocardiography studies to investigate regional myocardial temporal dynamics and diastology. While human readers' visualization predominantly focused on the apex of the heart in TTS patients, the DCNN temporal arm's saliency visualization was attentive on the base of the heart, particularly at the atrioventricular (AV) plane. Compared with STEMI patients, TTS patients consistently showed weaker peak longitudinal displacement (in pixels) in the basal inferoseptal (systolic: 2.15 ± 1.41 vs. 3.10 ± 1.66, P < 0.001; diastolic: 2.36 ± 1.71 vs. 2.97 ± 1.69, P = 0.004) and basal anterolateral (systolic: 2.70 ± 1.96 vs. 3.44 ± 2.13, P = 0.003; diastolic: 2.73 ± 1.70 vs. 3.45 ± 2.20, P = 0.002) segments, and worse longitudinal myocardial strain in the basal inferoseptal (-8.5 ± 3.8% vs. -9.9 ± 4.1%, P = 0.013) and basal anterolateral (-8.6 ± 4.2% vs. -10.4 ± 4.1%, P = 0.006) segments. Meanwhile, TTS patients showed worse diastolic mechanics than STEMI patients (E'/septal: 5.1 ± 1.2 cm/s vs. 6.3 ± 1.5 cm/s, P < 0.001; S'/septal: 5.8 ± 1.3 cm/s vs. 6.8 ± 1.4 cm/s, P < 0.001; E'/lateral: 6.0 ± 1.4 cm/s vs. 7.9 ± 1.6 cm/s, P < 0.001; S'/lateral: 6.3 ± 1.4 cm/s vs. 7.3 ± 1.5 cm/s, P < 0.001; E/E': 15.5 ± 5.6 vs. 12.5 ± 3.5, P < 0.001). Conclusion: The spatiotemporal DCNN saliency visualization helps identify the pattern of myocardial temporal dynamics and navigates the quantification of regional myocardial mechanics. Reduced AV plane displacement in TTS patients likely correlates with impaired diastolic mechanics.

Renal cell carcinoma and venous tumor thrombus: predicting sarcomatoid dedifferentiation through preoperative IVIM-based MR imaging.

PURPOSE: To evaluate the value of preoperative intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) and conventional MRI indicators in identifying sarcomatoid dedifferentiation in renal cell carcinoma (RCC) and tumor thrombus. METHODS: From September 2016 to April 2023, consecutive patients with RCC and tumor thrombus who received routine MRI examination and IVIM-DWI before radical resection were enrolled prospectively. Kaplan-Meier method with log-rank test was used to calculate and compare the survival probability. The preoperative imaging features were analyzed. Univariate and multivariable logistic regression analyses were employed to identify independent predictors of sarcomatoid dedifferentiation. The predictive ability was evaluated by receiver operating characteristic (ROC) curves. RESULTS: Twenty-two patients (15.3%) of the 144 patients in the training set (median age, 58.0 years [IQR, 52.0-65.0 years]; 108 men) and 11 patients (22.4%) of the 49 patients in the test set (median age, 58.0 years [IQR, 53.0-63.0 years]; 38 men) had sarcomatoid dedifferentiated tumors. Patients with sarcomatoid-differentiated tumors had poor progress-free survival in the training set and test set (P < 0.001 and P = 0.007). f value (P = 0.011), mN stage (P = 0.007), and necrosis (P = 0.041) were independent predictors for predicting sarcomatoid dedifferentiation in the training set. The model combining conventional MRI features and f value had AUCs of 0.832 (95% CI 0.755-0.909) and 0.825 (95% CI 0.702-0.948) in predicting sarcomatoid dedifferentiation in the training set and test set. CONCLUSION: It is feasible to preoperatively identify sarcomatoid dedifferentiation based on IVIM-DWI and conventional MR imaging indicators.

An Improved Nested U-Net Network for Fluorescence In Situ Hybridization Cell Image Segmentation.

Fluorescence in situ hybridization (FISH) is a powerful cytogenetic method used to precisely detect and localize nucleic acid sequences. This technique is proving to be an invaluable tool in medical diagnostics and has made significant contributions to biology and the life sciences. However, the number of cells is large and the nucleic acid sequences are disorganized in the FISH images taken using the microscope. Processing and analyzing images is a time-consuming and laborious task for researchers, as it can easily tire the human eyes and lead to errors in judgment. In recent years, deep learning has made significant progress in the field of medical imaging, especially the successful application of introducing the attention mechanism. The attention mechanism, as a key component of deep learning, improves the understanding and interpretation of medical images by giving different weights to different regions of the image, enabling the model to focus more on important features. To address the challenges in FISH image analysis, we combined medical imaging with deep learning to develop the SEAM-Unet++ automated cell contour segmentation algorithm with integrated attention mechanism. The significant advantage of this algorithm is that it improves the accuracy of cell contours in FISH images. Experiments have demonstrated that by introducing the attention mechanism, our method is able to segment cells that are adherent to each other more efficiently.

Magnetic Microbead-Based Herringbone Chip for Sensitive Detection of Human Immunodeficiency Virus.

The microfluidic chip-based nucleic acid detection method significantly improves the sensitivity since it precisely controls the microfluidic flow in microchannels. Nonetheless, significant challenges still exist in improving the detection efficiency to meet the demand for rapid detection of trace substances. This work provides a novel magnetic herringbone (M-HB) structure in a microfluidic chip, and its advantage in rapid and sensitive detection is verified by taking complementary DNA (cDNA) sequences of human immunodeficiency virus (HIV) detection as an example. The M-HB structure is designed based on controlling the magnetic field distribution in the micrometer scale and is formed by accumulation of magnetic microbeads (MMBs). Hence, M-HB is similar to a nanopore microstructure, which has a higher contact area and probe density. All of the above is conducive to improving sensitivity in microfluidic chips. The M-HB chip is stable and easy to form, which can linearly detect cDNA sequences of HIV quantitatively ranging from 1 to 20 nM with a detection limit of 0.073 nM. Compared to the traditional herringbone structure, this structure is easier to form and release by controlling the magnetic field, which is flexible and helps in further study. Results show that this chip can sensitively detect the cDNA sequences of HIV in blood samples, demonstrating that it is a powerful platform to rapidly and sensitively detect multiple nucleic acid-related viruses of infectious diseases.

Therapeutic effect and mechanism of Yougui Wan in rats with intervertebral disk degeneration.

OBJECTIVE: To explore the potential mechanism of Yougui Wan on deformed lumbar intervertebral disk structure in rats. METHODS: Thirty male Sprague-Dawley rats were randomly divided into 3 groups, with 10 rats in each group. The animals in the blank control group were healthy rats without specific treatment, and those in the model group and traditional Chinese medicine (TCM) group were used to establish the intervertebral disk degeneration (IDD) model by puncturing the annulus. Four weeks after modeling, rats in the TCM group were administered Yougui Wan by gavage for 2 consecutive weeks. Serum interleukin-6 (IL-10), macrophage migration inhibitory factor (MIF) and tumor necrosis factor alpha (TNF-α) levels were measured by ELISA, and the protein expression levels of collagen II and Notch1 in intervertebral disk tissues were examined by Western blotting. Apoptosis was detected by the TUNEL method. RESULTS: Compared with those in the blank group, IL-10, MIF and TNF-α levels in the model group and TCM group were increased (P < 0.05), the protein expression levels of collagen II were decreased, and the protein expression levels of Notch1 were increased. Compared with those in the model group, the levels of IL-10 in the TCM group were increased (P < 0.05), the levels of MIF and TNF-α were decreased (P < 0.05), the protein expression levels of collagen II were increased, and the protein expression levels of Notch1 were decreased. CONCLUSION: Yougui Wan can inhibit the inflammatory response in IDD rats, reduce the degradation of extracellular matrix, reduce apoptosis in nucleus pulposus cells, and alleviate intervertebral disk degeneration. The mechanism may be related to the regulation of the Notch signaling pathway.

EIF4A3-mediated biogenesis of circSTX6 promotes bladder cancer metastasis and cisplatin resistance.

BACKGROUND: Cisplatin (CDDP)-based chemotherapy is a standard first-line treatment for metastatic bladder cancer (BCa) patients, and chemoresistance remains a major challenge in clinical practice. Circular RNAs (circRNAs) have emerged as essential regulators in carcinogenesis and cancer progression. However, the role of circRNAs in mediating CDDP chemosensitivity has yet to be well elucidated in BCa. METHODS: CircSTX6 (hsa_circ_0007905) was identified by mining the public circRNA datasets and verified by Sanger sequencing, agarose gel electrophoresis, RNase R treatment and qRT-PCR assays. Then, function experiments were performed to evaluate the effects of circSTX6 on BCa metastasis. Luciferase reporter assay, RNA pull-down, RNA immunoprecipitation (RIP), RNA stability assay, Fluorescence in situ hybridization (FISH) and Immunofluorescence (IF) were conducted to evaluate the interaction among circSTX6, miR-515-3p, PABPC1 and SUZ12. Animal experiments were performed to explore the function of circSTX6 in tumor metastasis and CDDP sensitivity. RESULTS: We identified that circSTX6 was significantly upregulated in clinical samples and cells of BCa. Functionally, circSTX6 promoted cell migration and invasion both in vitro and in vivo. Mechanistically, circSTX6 could act as a miR-515-3p sponge and abolish its effect on SUZ12. Moreover, circSTX6 was confirmed to increase the stability of SUZ12 mRNA by interacting with a mRNA stabilizer PABPC1 and subsequently promote the expression of SUZ12. Importantly, silencing of circSTX6 improved the chemosensitivity of CDDP-resistant bladder cancer cells to CDDP. Furthermore, in vivo analysis supported that knockdown of circSTX6 attenuated CDDP resistance in BCa tumors. CONCLUSION: These studies demonstrate that circSTX6 plays a pivotal role in BCa metastasis and chemoresistance, and has potential to serve as a therapeutic target for treatment of BCa.

Assessment of Ki-67 proliferation index in prognosis prediction in patients with nonmetastatic clear cell renal cell carcinoma and tumor thrombus.

PURPOSE: To determine the optimal cut-off value of Ki-67 for predicting the survival of patients with clear cell renal cell carcinoma (ccRCC) and tumor thrombus and to explore the correlation between Ki-67 expression and pathological features. PATIENTS AND METHODS: We retrospectively analyzed Ki-67 immunohistochemical staining of ccRCC and tumor thrombus resected from February 2006 to February 2022. The survival rate was evaluated using the Kaplan-Meier method. The optimal cut-off value of the Ki-67 expression for predicting survival was determined by the minimum P-value method. Clinicopathological data were compared based on Ki-67 status (low versus high expression). Univariate and multivariate Cox regression analysis was used to explore independent predictors. RESULTS: A total of 202 patients (median age, 58 years [IQR, 52-65 years], 147 men) with ccRCC and tumor thrombus were included in the study. The optimal cut-off value of Ki-67 for predicting survival was 30%. 159 (78.7%) and 43 (21.3%) patients were included in the low-expression and high-expression groups. Patients with Ki-67 high expression had significantly worse recurrence-free survival (P < 0.001) and cancer-specific survival (P < 0.001). Ki-67 high expression was associated with adverse pathological features, including tumor necrosis, ISUP nuclear grade, sarcomatoid differentiation, perirenal fat invasion, renal pelvis invasion, and inferior vena cava wall invasion (all P < 0.050). Ki-67 expression ≥ 30% (P = 0.016), tumor side (P = 0.003), diabetes (P = 0.040), blood loss (P = 0.016), inferior vena cava wall invasion (P = 0.016), and sarcomatoid differentiation (P = 0.014) were independent predictors of cancer-specific survival. CONCLUSION: The optimal cut-off level of Ki-67 in predicting the prognosis of ccRCC and tumor thrombus was 30%. The high expression of Ki-67 was associated with the aggressive pathological phenotype and poor prognosis.

Detection of serum major histocompatibility complex I (HLA-1) and ß2-microglobulin (ß2M) in pre-eclampsia using isobaric tags for relative and absolute quantitation (iTRAQ).

OBJECTIVE: The purpose of this preliminary investigation into the pathogenesis of pre-eclampsia was to screen the differential proteins in the serum of pregnant women with normal pregnancy and early-onset pre-eclampsia using isobaric tags for relative and absolute quantitation (iTRAQ), so as to identify serum biomarkers for the early diagnosis of pre-eclampsia. METHODS: We examined the peripheral serum of 58 normal pregnant women and 42 pregnant women with early-onset pre-eclampsia using iTRAQ; the differentially expressed proteins were screened for bioinformatics analysis; and the expression of candidate proteins human leukocyte antigen-1 (HLA-1) and ß2-microglobulin (ß2M) in placental tissues was detected using western blot. RESULTS: We identified a total of 63 differential proteins in the serum of patients from the normal control group and the pre-eclampsia group, and this included 24 up-regulated proteins and 39 down-regulated proteins. The western blot results of placental tissue showed reduced HLA-1 expression (1.12 ± 0.23) in the placenta in the pre-eclampsia group as compared with the normal control group (1.34 ± 0.22). Consistent with the results observed in the serum, ß2M in the placenta in the pre-eclampsia group was significantly elevated (1.05 ± 0.47) in comparison with the normal group (0.75 ± 0.33) (P < 0.05). CONCLUSION: In this study, we found that iTRAQ technology was useful for identifying differentially expressed proteins in the peripheral serum of pregnant women with pre-eclampsia, and that HLA-1 and ß2M, which may be involved in the occurrence of pre-eclampsia, show promise as predictive markers of pre-eclampsia.