Cleanroom‐Free Direct Laser Micropatterning of Polymers for Organic Electrochemical Transistors in Logic Circuits and Glucose Biosensors

Organic electrochemical transistors (OECTs) are promising devices for bioelectronics, such as biosensors. However, current cleanroom-based microfabrication of OECTs hinders fast prototyping and widespread adoption of this technology for low-volume, low-cost applications. To address this limitation, a versatile and scalable approach for ultrafast laser microfabrication of OECTs is herein reported, where a femtosecond laser to pattern insulating polymers (such as parylene C or polyimide) is first used, exposing the underlying metal electrodes serving as transistor terminals (source, drain, or gate). After the first patterning step, conducting polymers, such as poly(3,4-ethylenedioxythiophene):poly(styrene sulfonate) (PEDOT:PSS), or semiconducting polymers, are spin-coated on the device surface. Another femtosecond laser patterning step subsequently defines the active polymer area contributing to the OECT performance by disconnecting the channel and gate from the surrounding spin-coated film. The effective OECT width can be defined with high resolution (down to 2 mu m) in less than a second of exposure. Micropatterning the OECT channel area significantly improved the transistor switching performance in the case of PEDOT:PSS-based transistors, speeding up the devices by two orders of magnitude. The utility of this OECT manufacturing approach is demonstrated by fabricating complementary logic (inverters) and glucose biosensors, thereby showing its potential to accelerate OECT research.Ultrafast focused femtosecond laser has been introduced for the direct micropatterning of organic electrochemical transistors (OECTs), providing high resolution (2 mu m), selective cleanroom-free patterning of insulating and conjugated polymer layers while preserving device operation, and high flexibility in device design. The approach has been validated in the fabrication of complementary inverters and glucose biosensors.imag

Fast deep learning reconstruction techniques for preclinical magnetic resonance fingerprinting

We propose a deep learning (DL) model and a hyperparameter optimization strategy to reconstruct T1 and T2 maps acquired with the magnetic resonance fingerprinting (MRF) methodology. We applied two different MRF sequence routines to acquire images of ex vivo rat brain phantoms using a 7-T preclinical scanner. Subsequently, the DL model was trained using experimental data, completely excluding the use of any theoretical MRI signal simulator. The best combination of the DL parameters was implemented by an automatic hyperparameter optimization strategy, whose key aspect is to include all the parameters to the fit, allowing the simultaneous optimization of the neural network architecture, the structure of the DL model, and the supervised learning algorithm. By comparing the reconstruction performances of the DL technique with those achieved from the traditional dictionary-based method on an independent dataset, the DL approach was shown to reduce the mean percentage relative error by a factor of 3 for T1 and by a factor of 2 for T2, and to improve the computational time by at least a factor of 37. Furthermore, the proposed DL method enables maintaining comparable reconstruction performance, even with a lower number of MRF images and a reduced k-space sampling percentage, with respect to the dictionary-based method. Our results suggest that the proposed DL methodology may offer an improvement in reconstruction accuracy, as well as speeding up MRF for preclinical, and in prospective clinical, investigations.We proposed a deep learning (DL) method and an optimization strategy for the reconstruction of T1 and T2 maps acquired with preclinical magnetic resonance fingerprinting (MRF) sequences. Compared with the traditional dictionary-based method, the DL approach improved the estimation of the maps, and reduced the computational time required for estimation. Moreover, our DL method allowed us to maintain comparable reconstruction performance, even with compressed MRF acquisition sequences.imag

Developing resilience of MNEs: From global value chain (GVC) capability and performance perspectives

External shocks create various risks for enterprises. Multinational enterprises (MNEs) work to develop resilience and improve their global risk management capability. The COVID-19 pandemic has compelled MNEs to improve their global value chain (GVC) capability to enhance their global risk management and operational performance, which could eventually impact their overall performance. Developing GVC capability is a challenge for MNEs. This study aims to examine the influence of global risk management capability on MNEs’ GVC capability to become more resilient to withstand such crises and further enhance their performance. Building on the resource- based view (RBV), dynamic capability view (DCV), and the existing literature, a conceptual research model was prepared. The model was then validated using the PLS-SEM technique to analyze the responses of the 323 managers at different MNEs. The study found a significant positive impact of global risk management capability on GVC capability, which eventually impacted MNE performance

Is motherhood still possible after pelvic carbon ion radiotherapy? A promising combined fertility-preservation approach

Introduction: Preserving the endocrine and reproductive function in young female cancer patients undergoing pelvic radiation is a significant challenge. While the photon beam radiation's adverse effects on the uterus and ovaries are well established, the impact of pelvic carbon ion radiotherapy on women's reproductive function is largely unexplored. Strategies such as oocyte cryopreservation and ovarian transposition are commonly recommended for safeguarding future fertility. Methods: This study presents a pioneering case of successful pregnancy after carbon ion radiotherapy for locally advanced sacral chondrosarcoma. Results: A multidisciplinary approach facilitated the displacement of ovaries and uterus before carbon ion radiotherapy, resulting in the preservation of endocrine and reproductive function. Conclusion: The patient achieved optimal oncological response and delivered a healthy infant following the completion of cancer treatment

Unlocking cardiac motion: assessing software and machine learning for single-cell and cardioid kinematic insights

The heart coordinates its functional parameters for optimal beat-to-beat mechanical activity. Reliable detection and quantification of these parameters still represent a hot topic in cardiovascular research. Nowadays, computer vision allows the development of open-source algorithms to measure cellular kinematics. However, the analysis software can vary based on analyzed specimens. In this study, we compared different software performances in in-silico model, in-vitro mouse adult ventricular cardiomyocytes and cardioids. We acquired in-vitro high-resolution videos during suprathreshold stimulation at 0.5-1-2 Hz, adapting the protocol for the cardioids. Moreover, we exposed the samples to inotropic and depolarizing substances. We analyzed in-silico and in-vitro videos by (i) MUSCLEMOTION, the gold standard among open-source software; (ii) CONTRACTIONWAVE, a recently developed tracking software; and (iii) ViKiE, an in-house customized video kinematic evaluation software. We enriched the study with three machine-learning algorithms to test the robustness of the motion-tracking approaches. Our results revealed that all software produced comparable estimations of cardiac mechanical parameters. For instance, in cardioids, beat duration measurements at 0.5 Hz were 1053.58 ms (MUSCLEMOTION), 1043.59 ms (CONTRACTIONWAVE), and 937.11 ms (ViKiE). ViKiE exhibited higher sensitivity in exposed samples due to its localized kinematic analysis, while MUSCLEMOTION and CONTRACTIONWAVE offered temporal correlation, combining global assessment with time-efficient analysis. Finally, machine learning reveals greater accuracy when trained with MUSCLEMOTION dataset in comparison with the other software (accuracy > 83%). In conclusion, our findings provide valuable insights for the accurate selection and integration of software tools into the kinematic analysis pipeline, tailored to the experimental protocol

Uniqueness of the invariant measure and asymptotic stability for the 2d Navier-Stokes equations with multiplicative noise

We establish the uniqueness and the asymptotic stability of the invariant measure for the two-dimensional Navier-Stokes equations driven by a multiplicative noise which is either bounded or with a sublinear or a linear growth. We work on an “effectively elliptic” setting, that is, we require that the range of the covariance operator contains the unstable directions. We exploit the generalized asymptotic coupling techniques of [12] and [16], used by these authors for the stochastic Navier-Stokes equations with additive noise. Here, we show how these methods are flexible enough to deal with multiplicative noise as well. A crucial role in our argument is played by the Foias-Prodi estimate in expected value, which has a different form (exponential or polynomial decay) according to the growth condition of the multiplicative noise