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The ability to tune material properties using gating by electric fields is at the heart of modern electronic technology. It is also a driving force behind recent advances in two-dimensional systems, such as the observation of gate electric-field-induced superconductivity and metal–insulator transitions. Here, we describe an ionic field-effect transistor (termed an iFET), in which gate-controlled Li ion intercalation modulates the material properties of layered crystals of 1T-TaS 2 . The strong charge doping induced by the tunable ion intercalation alters the energetics of various charge-ordered states in 1T-TaS 2 and produces a series of phase transitions in thin-flake samples with reduced dimensionality. We find that the charge-density wave states in 1T-TaS 2 collapse in the two-dimensional limit at critical thicknesses. Meanwhile, at low temperatures, the ionic gating induces multiple phase transitions from Mott-insulator to metal in 1T-TaS 2 thin flakes, with five orders of magnitude modulation in resistance, and superconductivity emerges in a textured charge-density wave state induced by ionic gating. Our method of gate-controlled intercalation opens up possibilities in searching for novel states of matter in the extreme charge-carrier-concentration limit. The high charge doping achieved in ionic field-effect transistors by lithium intercalation allows gate-controlled phase transitions in thin flakes of 1T-TaS 2 .

Electron–electron and electron–phonon interactions are two major driving forces that stabilize various charge-ordered phases of matter. In layered compound 1T-TaS 2 , the intricate interplay between the two generates a Mott-insulating ground state with a peculiar charge-density-wave (CDW) order. The delicate balance also makes it possible to use external perturbations to create and manipulate novel phases in this material. Here, we study a mosaic CDW phase induced by voltage pulses, and find that the new phase exhibits electronic structures entirely different from that of the original Mott ground state. The mosaic phase consists of nanometre-sized domains characterized by well-defined phase shifts of the CDW order parameter in the topmost layer, and by altered stacking relative to the layers underneath. We discover that the nature of the new phase is dictated by the stacking order, and our results shed fresh light on the origin of the Mott phase in 1T-TaS 2 . In correlated materials, new phases emerge when the balance between many-body interactions is perturbed. Here, Ma et al . induce a mosaic charge-density-wave phase out of Mott insulating state in layered 1T-TaS 2 by voltage pulses, which reveals a dominating role of interlayer stacking order.

The quest to replace animal models used in drug testing owing to their lack of accuracy in reflecting human physiology, and the higher comparative cost and time involved in testing with such animal models has given rise to the organ-on-a-chip technology. Organ-on-a-chip-based microphysiological systems are flexible and can be engineered to specifically mimic desired organs and tissue types for the drug discovery and development process. Kidney-specific and non-specific drugs either directly or indirectly affect the kidneys’ function by inducing kidney injury. It is quite challenging to integrate electrochemical sensors in the microphysiological systems for continuous monitoring of micro-environment metabolism. We present a theranostic proximal tubule-on-a-chip model for live monitoring of cellular growth pattern. The sensors monitored real-time changes under disease condition and drug treatment based upon cell adhesion and culture medium pH. A glass-based microfluidic chip was designed with integrated transparent electrodes for transepithelial electrical resistance (TEER) monitoring. Additionally, an optical pH sensor and a microscope have been added in the platform for the real-time monitoring of the tissue. This model has the potential to study the absorption and metabolism of the drug along with the capacity to complete and optimize its toxicity assessment.

The precise prediction of acute kidney injury (AKI) after nephrectomy for renal cell carcinoma (RCC) is an important issue because of its relationship with subsequent kidney dysfunction and high mortality. Herein we addressed whether machine learning (ML) algorithms could predict postoperative AKI risk better than conventional logistic regression (LR) models. A total of 4104 RCC patients who had undergone unilateral nephrectomy from January 2003 to December 2017 were reviewed. ML models such as support vector machine, random forest, extreme gradient boosting, and light gradient boosting machine (LightGBM) were developed, and their performance based on the area under the receiver operating characteristic curve, accuracy, and F1 score was compared with that of the LR-based scoring model. Postoperative AKI developed in 1167 patients (28.4%). All the ML models had higher performance index values than the LR-based scoring model. Among them, the LightGBM model had the highest value of 0.810 (0.783–0.837). The decision curve analysis demonstrated a greater net benefit of the ML models than the LR-based scoring model over all the ranges of threshold probabilities. The application of ML algorithms improves the predictability of AKI after nephrectomy for RCC, and these models perform better than conventional LR-based models.

Acute kidney injury (AKI) presents a significant clinical challenge due to its rapid progression to kidney failure, resulting in serious complications such as electrolyte imbalances, fluid overload, and the potential need for renal replacement therapy. Early detection and prediction of AKI can improve patient outcomes through timely interventions. This review was conducted as a narrative literature review, aiming to explore state-of-the-art models for early detection and prediction of AKI. We conducted a comprehensive review of findings from various studies, highlighting their strengths, limitations, and practical considerations for implementation in healthcare settings. We highlight the potential benefits and challenges of their integration into routine clinical care and emphasize the importance of establishing robust early-detection systems before the introduction of artificial intelligence (AI)-assisted prediction models. Advances in AI for AKI detection and prediction are examined, addressing their clinical applicability, challenges, and opportunities for routine implementation.

We study the catalytic activity properties of Pt doped and alloyed with 3 d transition metal (Cr, Mn, and Fe). Using first-principles calculations, we investigate energetically favored configurations of doped Pt, including dopant locations in the Pt layers and distances between neighboring dopants. In a wide range of doping concentrations, our calculations on surface d -band centers reveal that the catalytic activity of the doped Pt is not considerably affected by transition metal impurities. In particular, Pt–Fe (1:1) alloys in an ordered face-centered tetragonal phase exhibit d -band centers aligned to the ideal value for the oxygen reduction reaction, which contrasts with disordered alloys whose d -band centers significantly deviate from the ideal one. This work suggests that doping and alloying Pt with transition metals is a promising route to design more affordable materials with lower Pt loading, while maintaining comparable to or even improved catalytic activity compared to pristine Pt.

Hyponatremia is primarily a water balance disorder associated with high morbidity and mortality. The pathophysiological mechanisms behind hyponatremia are multifactorial, and diagnosing and treating this disorder remains challenging. In this review, the classification, pathogenesis, and step-by-step management approaches for hyponatremia in patients with liver disease are described based on recent evidence. We summarize the five sequential steps of the traditional diagnostic approach: 1) confirm true hypotonic hyponatremia, 2) assess the severity of hyponatremia symptoms, 3) measure urine osmolality, 4) classify hyponatremia based on the urine sodium concentration and extracellular fluid status, and 5) rule out any coexisting endocrine disorder and renal failure. Distinct treatment strategies for hyponatremia in liver disease should be applied according to the symptoms, duration, and etiology of disease. Symptomatic hyponatremia requires immediate correction with 3% saline. Asymptomatic chronic hyponatremia in liver disease is prevalent and treatment plans should be individualized based on diagnosis. Treatment options for correcting hyponatremia in advanced liver disease may include water restriction; hypokalemia correction; and administration of vasopressin antagonists, albumin, and 3% saline. Safety concerns for patients with liver disease include a higher risk of osmotic demyelination syndrome.

A young man with smoldering multiple myeloma died of hypotensive shock 2.5 days after severe acute respiratory syndrome coronavirus 2 vaccination. Clinical findings suggested systemic capillary leak syndrome (SCLS); the patient had experienced a previous suspected flare episode. History of SCLS may indicate higher risk for SCLS after receiving this vaccine.

Metformin is the primary treatment for type 2 diabetes mellitus (T2DM) due to its effectiveness in improving clinical outcomes in patients with preserved renal function, however, the evidence on the effectiveness of metformin in various renal functions is lacking. We performed a retrospective, multicenter, observational study used data of patients with T2DM obtained from three tertiary hospitals’ databases. Patients given metformin within run-in periods and with at least one additional prescription formed the metformin cohort. A control cohort comprised those prescribed oral hypoglycemic agents other than metformin and never subsequently received a metformin prescription within observation period. For patients without diabetic nephropathy (DN), the outcomes included events of DN, major adverse cardiovascular events (MACE), and major adverse kidney events (MAKE). After 1:1 propensity matching, 1994 individuals each were selected for the metformin and control cohorts among T2DM patients without baseline DN. The incidence rate ratios (IRR) for DN, MACEs, and MAKEs between cohorts were 1.06 (95% CI 0.96–1.17), 0.76 (0.64–0.92), and 0.45 (0.33–0.62), respectively. In cohorts with renal function of CKD 3A, 3B, and 4, summarized IRRs of MACEs and MAKEs were 0.70 (0.57–0.87) and 0.39 (0.35–0.43) in CKD 3A, 0.83 (0.74–0.93) and 0.44 (0.40–0.48) in CKD 3B, and 0.71 (0.60–0.85) and 0.45 (0.39–0.51) in CKD 4. Our research indicates that metformin use in T2DM patients across various renal functions consistently correlates with a decreased risk of overt DN, MACE, and MAKE.

Postoperative acute kidney injury (PO-AKI) prediction models for non-cardiac major surgeries typically rely solely on preoperative clinical characteristics. In this study, we developed and externally validated a deep-learning-based model that integrates preoperative data with minute-scale intraoperative vital signs to predict PO-AKI. Using data from three hospitals, we constructed a convolutional neural network-based EfficientNet framework to analyze intraoperative data and created an ensemble model incorporating 103 baseline variables of demographics, medication use, comorbidities, and surgery-related characteristics. Model performance was compared with the conventional SPARK model from a previous study. Among 110,696 patients, 51,345 were included in the development cohort, and 59,351 in the external validation cohorts. The median age of the cohorts was 60, 61, and 66 years, respectively, with males comprising 54.9%, 50.8%, and 42.7% of each cohort. The intraoperative vital sign-based model demonstrated comparable predictive power (AUROC (Area Under the Receiver Operating Characteristic Curve): discovery cohort 0.707, validation cohort 0.637 and 0.607) to preoperative-only models (AUROC: discovery cohort 0.724, validation cohort 0.697 and 0.745). Adding 11 key clinical variables (e.g., age, sex, estimated glomerular filtration rate (eGFR), albuminuria, hyponatremia, hypoalbuminemia, anemia, diabetes, renin-angiotensin-aldosterone inhibitors, emergency surgery, and the estimated surgery time) improved the model's performance (AUROC: discovery cohort 0.765, validation cohort 0.716 and 0.761). The ensembled deep-learning model integrating both preoperative and intraoperative data achieved the highest predictive accuracy (AUROC: discovery cohort 0.795, validation cohort 0.762 and 0.786), outperforming the conventional SPARK model. The retrospective design in a single-nation cohort with non-inclusion of some potential AKI-associated variables is the main limitation of this study. This deep-learning-based PO-AKI risk prediction model provides a comprehensive approach to evaluating PO-AKI risk prediction by combining preoperative clinical data with real-time intraoperative vital sign information, offering enhanced predictive performance for better clinical decision-making.