Applied insight: studying reducing the carbon footprint of the drying process and its environmental impact and financial return.

Harnessing solar energy is one of the most important practical insights highlighted to mitigate the severe climate change (CC) phenomenon. Therefore, this study aims to focus on the use of hybrid solar dryers (HSDs) within an environmentally friendly framework, which is one of the promising applications of solar thermal technology to replace traditional thermal technology that contributes to increasing the severity of the CC phenomenon. The HSD, based on a traditional electrical energy source (HSTEE) and electrical energy from photovoltaic panels (HSPVSE), was evaluated compared to a traditional electrical (TE) dryer for drying some medicinal and aromatic plants (MAPs). This is done by evaluating some of the drying outputs, energy consumed, carbon footprint, and financial return at 30, 40, and 50°C. The best quality of dried MAP samples in terms of essential oil (EO, %) and microbial load was achieved at 40°C. The HSTEE dryer has reduced energy consumption compared to the TE dryer by a percentage ranging from 37% to 54%. The highest CO2 mitigated ratio using the HSTEE dryer was recorded in thyme, marjoram, and lemongrass samples, with values ranging from 45% to 54% at 30, 40, and 50°C. The highest financial return obtained from energy consumption reduction and carbon credit footprint was achieved at 50°C, with values ranging from 5,313.69 to 6,763.03 EGP/year (EGP ≈ 0.0352 USD) when coal was used as a fuel source for the generation of electricity. Moreover, the HSPVSE dryer achieved a 100% reduction in traditional energy consumption and then reduced CO2 emissions by 100%, which led to a 100% financial return from both energy reduction and carbon credit. The highest financial returns were observed at 50°C, with values ranging from 13,872.56 to 15,007.02, 12,927.28 to 13,984.43, and 11,981.99 to 12,961.85 EGP/year (EGP ≈ 0.0352 USD) for coal, oil, and natural gas, respectively. The HS dryers show potential for environmental conservation contribution; furthermore, earning money from energy savings and carbon credit could help improve the living standards and maximize benefits for stakeholders.

A novel fast hybrid capture sequencing method for high-efficiency common human coronavirus whole-genome acquisition.

Rapid and accurate sequencing of the entire viral genome, coupled with continuous monitoring of genetic changes, is crucial for understanding the epidemiology of coronaviruses. We designed a novel method called micro target hybrid capture system (MT-Capture) to enable whole-genome sequencing in a timely manner. The novel design of probes used in target binding exhibits a unique and synergistic "hand-in-hand" conjugation effect. The entire hybrid capture process is within 2.5 hours, overcoming the time-consuming and complex operation characteristics of the traditional liquid-phase hybrid capture (T-Capture) system. By designing specific probes for these coronaviruses, MT-Capture effectively enriched isolated strains and 112 clinical samples of coronaviruses with cycle threshold values below 37. Compared to multiplex PCR sequencing, it does not require frequent primer updates and has higher compatibility. MT-Capture is highly sensitive and capable of tracking variants.IMPORTANCEMT-Capture is meticulously designed to enable the efficient acquisition of the target genome of the common human coronavirus. Coronavirus is a kind of virus that people are generally susceptible to and is epidemic and infectious, and it is the virus with the longest genome among known RNA viruses. Therefore, common human coronavirus samples are selected to evaluate the accuracy and sensitivity of MT-Capture. This method utilizes innovative probe designs optimized through probe conjugation techniques, greatly shortening the time and simplifying the handwork compared with traditional hybridization capture processes. Our results demonstrate that MT-Capture surpasses multiplex PCR in terms of sensitivity, exhibiting a thousandfold increase. Moreover, MT-Capture excels in the identification of mutation sites. This method not only is used to target the coronaviruses but also may be used to diagnose other diseases, including various infectious diseases, genetic diseases, or tumors.

Transumbilical single-incision hybrid pyeloplasty for infant ureteropelvic junction obstruction.

Ureteropelvic junction obstruction (UPJO) can be treated by various pyeloplasty techniques. We present a hybrid technique incorporating elements of laparoendoscopic single-site surgery and open pyeloplasty through a single umbilical incision. As a result, seven infants with UPJO underwent the hybrid pyeloplasty smoothly. The mean operative time was 131.9 min. At a follow-up of 11.8-50.0 months, all infants showed significant improvement and no symptoms except for one febrile urinary tract infection. The cosmetic results were very satisfactory without obvious visible scars. Therefore, the hybrid pyeloplasty appears to be a simple and effective minimally invasive surgery for treating infant UPJO.

A Tellurium-Boosted High-Areal-Capacity Zinc-Sulfur Battery.

Aqueous rechargeable zinc-sulfur (Zn-S) batteries are a promising, cost-effective, and high-capacity energy storage technology. Still, they are challenged by the poor reversibility of S cathodes, sluggish redox kinetics, low S utilization, and unsatisfactory areal capacity. This work develops a facile strategy to achieve an appealing high-areal-capacity (above 5 mAh cm-2) Zn-S battery by molecular-level regulation between S and high-electrical-conductivity tellurium (Te). The incorporation of Te as a dopant allows for manipulation of the Zn-S electrochemistry, resulting in accelerated redox conversion, and enhanced S utilization. Meanwhile, accompanied by the S-ZnS conversion, Te is converted to zinc telluride during the discharge process, as revealed by ex-situ characterizations. This additional redox reaction contributes to the S cathode's total excellent discharge capacity. With this unique cathode structure design, the carbon-confined TeS cathode (denoted as Te1S7/C) delivers a high reversible capacity of 1335.0 mAh g-1 at 0.1 A g-1 with a mass loading of 4.22 mg cm-2, corresponding to a remarkable areal capacity of 5.64 mAh cm-2. Notably, a hybrid electrolyte design uplifts discharge plateau, reduces overpotential, suppresses Zn dendrites growth, and extends the calendar life of Zn-Te1S7 batteries. This study provides a rational S cathode structure to realize high-capacity Zn-S batteries for practical applications.

Designing a deep hybridized residual and SE model for MRI image-based brain tumor prediction.

Deep learning techniques have become crucial in the detection of brain tumors but classifying numerous images is time-consuming and error-prone, impacting timely diagnosis. This can hinder the effectiveness of these techniques in detecting brain tumors in a timely manner. To address this limitation, this study introduces a novel brain tumor detection system. The main objective is to overcome the challenges associated with acquiring a large and well-classified dataset. The proposed approach involves generating synthetic Magnetic Resonance Imaging (MRI) images that mimic the patterns commonly found in brain MRI images. The system utilizes a dataset consisting of small images that are unbalanced in terms of class distribution. To enhance the accuracy of tumor detection, two deep learning models are employed. Using a hybrid ResNet+SE model, we capture feature distributions within unbalanced classes, creating a more balanced dataset. The second model, a tailored classifier identifies brain tumors in MRI images. The proposed method has shown promising results, achieving a high detection accuracy of 98.79%. This highlights the potential of the model as an efficient and cost-effective system for brain tumor detection.

Effect of Thermo- and pH-sensitive Block Copolymer Structure and Composition on the Synthesis and Stabilization of Gold Nanoparticles.

Homopolymers of poly[N-(2-(diethylamino)ethyl) acrylamide] exhibit the ability to adsorb onto the surface of preformed or growing gold nanoparticles. The resulting hybrid materials possess a pH and thermo-sensitive nature. Consequently, their optical properties can be modulated by manipulating either the temperature or the pH. Moreover, introducing monomers based on poly(N-isopropyl acrylamide) into block or random statistical polymers enables further modulation of the thermosensitive properties. These copolymers, employed for the in-situ synthesis and/or stabilization of gold nanoparticles, lead to hybrid materials whose properties and/or particle size depend on the polymer composition and microstructure: statistical polymers emerge as superior stabilizing agents compared to their block counterparts at a constant composition.

Comparative Effectiveness of Hybrid Closed-Loop Automated Insulin Delivery Systems Among Patients with Type 1 Diabetes.

BACKGROUND: Clinical trials have demonstrated the efficacy and safety of hybrid closed-loop (HCL) systems, yet few studies have compared outcomes in the real-world setting. METHOD: This retrospective study analyzed patients from an academic endocrinology practice between January 1, 2018, and November 18, 2022. The inclusion criteria were diagnosis code for type I diabetes (T1D), >18 years of age, new to any HCL system [Medtronic 670G/770G (MT), Tandem Control IQ (CIQ), or Omnipod 5 (OP5)], and availability of a pump download within three months. The outcomes included %time in range (TIR) of 70 to 180 mg/dL, %time below range (TBR) <70 mg/dL at 90 days, and HbA1c for 91 to 180 days. RESULT: Of the 176 participants, 47 were MT, 74 CIQ, and 55 OP5. Median (25%, 75%) change in HbA1c was -0.1 (-0.8, 0.3), -0.6 (-1.1, -0.15), and -0.55 (-0.98, 0)% for MT, CIQ, and OP5, respectively, (P = .04). TIR was 70 (57, 76), 67 (59, 75), and 68 (60, 76)% (P = .95) at 90 days while TBR was 2 (1, 3), 1 (0, 2), and 1 (0, 1)%, respectively, (P = .002). The %time in automated delivery was associated with TIR and change in HbA1c. After controlling other factors including %time in automated delivery, HCL type was not an independent predictor of change in HbA1c nor TIR but remained a significant predictor of TBR. CONCLUSION: There were significant reductions in HbA1c in CIQ and OP5. TIR was similar across pumps, but TBR was highest with MT. The %time in automated delivery likely explains differences in change in HbA1c but not TBR between HCL systems.

Statistical inference for Nadarajah-Haghighi distribution under unified hybrid censored competing risks data.

Nadarajah and Haghighi distribution (NHD) inferences problem has been discussed under unified hybrid censoring scheme (UHCS) in the existence of competing risks model. Competing risks model is defined by time-to-failure under more than one cause of failure, which can be dependent or independent. This study focuses on discussing the case of failure partially observed causes of failure competing risks model. We obtain various inferences: we first obtain the MLE, in addition, we construct approximate confidence intervals (ACIs). Second, we obtain the Bayes estimator via SELF and related highest posterior density (HPD) using Markov Chain Monte Carlo (MCMC). Finally, an electrical appliances data set and simulation studies have been analyzed for further illustrations.

Ether-Water Co-Solvent Electrolytes Enhanced Vanadium Oxide Cathode Cyclic Behaviors for Zinc Batteries.

Vanadium-based compounds are fantastic cathodes for aqueous zinc metal batteries due to the high specific capacity and excellent rate capability. Nevertheless, the practical application has been hampered by the dissolution of vanadium in traditional aqueous electrolytes owing to the strong polarity of water molecules. Herein, we propose a hybrid electrolyte made of Zn(ClO4)2 salt in tetraethylene glycol dimethyl ether (G4) and H2O solvents to upgrade the cycle life of Zn//K0.486V2O5 battery. The G4 jointly solvates with Zn2+ ions and replaces a portion of the H2O molecules in the Zn2+ solvation sheath. It forms a strong bond with H2O, reducing its activity, and significantly inhibiting vanadium dissolution and water-induced parasitic reaction. Consequently, the optimized electrolyte with H2O and G4 volume ratio of 5:5 enhances the cycling stability of Zn//K0.486V2O5 battery, enabling it to reach up to 600 cycles. In addition, the battery demonstrates a satisfactory reversible capacity of 475.7 mAh g-1 and excellent rate performance attributed to the moderate ionic conductivity (28.8 mS cm-1) of the hybrid electrolyte. Last but not least, in the optimized electrolyte, the symmetric Zn//Zn cells deliver a long cycling performance of 400 h, while the asymmetric Zn//Cu cells shows a high average coulombic efficiency of 97.4%.

Simultaneous targeted and discovery-driven clinical proteotyping using hybrid-PRM/DIA.

BACKGROUND: Clinical samples are irreplaceable, and their transformation into searchable and reusable digital biobanks is critical for conducting statistically empowered retrospective and integrative research studies. Currently, mainly data-independent acquisition strategies are employed to digitize clinical sample cohorts comprehensively. However, the sensitivity of DIA is limited, which is why selected marker candidates are often additionally measured targeted by parallel reaction monitoring. METHODS: Here, we applied the recently co-developed hybrid-PRM/DIA technology as a new intelligent data acquisition strategy that allows for the comprehensive digitization of rare clinical samples at the proteotype level. Hybrid-PRM/DIA enables enhanced measurement sensitivity for a specific set of analytes of current clinical interest by the intelligent triggering of multiplexed parallel reaction monitoring (MSxPRM) in combination with the discovery-driven digitization of the clinical biospecimen using DIA. Heavy-labeled reference peptides were utilized as triggers for MSxPRM and monitoring of endogenous peptides. RESULTS: We first evaluated hybrid-PRM/DIA in a clinical context on a pool of 185 selected proteotypic peptides for tumor-associated antigens derived from 64 annotated human protein groups. We demonstrated improved reproducibility and sensitivity for the detection of endogenous peptides, even at lower concentrations near the detection limit. Up to 179 MSxPRM scans were shown not to affect the overall DIA performance. Next, we applied hybrid-PRM/DIA for the integrated digitization of biobanked melanoma samples using a set of 30 AQUA peptides against 28 biomarker candidates with relevance in molecular tumor board evaluations of melanoma patients. Within the DIA-detected approximately 6500 protein groups, the selected marker candidates such as UFO, CDK4, NF1, and PMEL could be monitored consistently and quantitatively using MSxPRM scans, providing additional confidence for supporting future clinical decision-making. CONCLUSIONS: Combining PRM and DIA measurements provides a new strategy for the sensitive and reproducible detection of protein markers from patients currently being discussed in molecular tumor boards in combination with the opportunity to discover new biomarker candidates.

The polyextremophile Natranaerobius thermophilus adopts a dual adaptive strategy to long-term salinity stress, simultaneously accumulating compatible solutes and K.

The bacterium Natranaerobius thermophilus is an extremely halophilic alkalithermophile that can thrive under conditions of high salinity (3.3-3.9 M Na+), alkaline pH (9.5), and elevated temperature (53°C). To understand the molecular mechanisms of salt adaptation in N. thermophilus, it is essential to investigate the protein, mRNA, and key metabolite levels on a molecular basis. Based on proteome profiling of N. thermophilus under 3.1, 3.7, and 4.3 M Na+ conditions compared to 2.5 M Na+ condition, we discovered that a hybrid strategy, combining the "compatible solute" and "salt-in" mechanisms, was utilized for osmotic adjustment dur ing the long-term salinity adaptation of N. thermophilus. The mRNA level of key proteins and the intracellular content of compatible solutes and K+ support this conclusion. Specifically, N. thermophilus employs the glycine betaine ABC transporters (Opu and ProU families), Na+/solute symporters (SSS family), and glutamate and proline synthesis pathways to adapt to high salinity. The intracellular content of compatible solutes, including glycine betaine, glutamate, and proline, increases with rising salinity levels in N. thermophilus. Additionally, the upregulation of Na+/ K+/ H+ transporters facilitates the maintenance of intracellular K+ concentration, ensuring cellular ion homeostasis under varying salinities. Furthermore, N. thermophilus exhibits cytoplasmic acidification in response to high Na+ concentrations. The median isoelectric points of the upregulated proteins decrease with increasing salinity. Amino acid metabolism, carbohydrate and energy metabolism, membrane transport, and bacterial chemotaxis activities contribute to the adaptability of N. thermophilus under high salt stress. This study provides new data that support further elucidating the complex adaptation mechanisms of N. thermophilus under multiple extremes.IMPORTANCEThis study represents the first report of simultaneous utilization of two salt adaptation mechanisms within the Clostridia class in response to long-term salinity stress.

Quantifying H&E staining results, grading and predicting IDH mutation status of gliomas using hybrid multi-dimensional MRI.

OBJECTIVE: To assess the performance of hybrid multi-dimensional magnetic resonance imaging (HM-MRI) in quantifying hematoxylin and eosin (H&E) staining results, grading and predicting isocitrate dehydrogenase (IDH) mutation status of gliomas. MATERIALS AND METHODS: Included were 71 glioma patients (mean age, 50.17 ± 13.38 years; 35 men). HM-MRI images were collected at five different echo times (80-200 ms) with seven b-values (0-3000 s/mm2). A modified three-compartment model with very-slow, slow and fast diffusion components was applied to calculate HM-MRI metrics, including fractions, diffusion coefficients and T2 values of each component. Pearson correlation analysis was performed between HM-MRI derived fractions and H&E staining derived percentages. HM-MRI metrics were compared between high-grade and low-grade gliomas, and between IDH-wild and IDH-mutant gliomas. Using receiver operational characteristic (ROC) analysis, the diagnostic performance of HM-MRI in grading and genotyping was compared with mono-exponential models. RESULTS: HM-MRI metrics FDvery-slow and FDslow demonstrated a significant correlation with the H&E staining results (p < .05). Besides, FDvery-slow showed the highest area under ROC curve (AUC = 0.854) for grading, while Dslow showed the highest AUC (0.845) for genotyping. Furthermore, a combination of HM-MRI metrics FDvery-slow and T2Dslow improved the diagnostic performance for grading (AUC = 0.876). DISCUSSION: HM-MRI can aid in non-invasive diagnosis of gliomas.

Hybrid conditioning of ionic liquid coupling with H2SO4 to improve the dewatering performance of municipal sludge.

Municipal sludge generated from wastewater treatment plants can cause a serious environmental and economic burden. A novel hybrid conditioning strategy was developed to enhance the dewatering performance of sludge, employing 1-butyl-3-methylimidazolium trifluoromethanesulfonate ([C4mim][CF3SO3]) treatment combined with H2SO4 acidification. Following conditioning, the capillary suction time ( CST normalized ), the specific resistance of filtration (SRF), and moisture content of the treated sludge were decreased to 1.99 ± 0.24 (s·L/g TSS), 1.33 ± 0.05 (1012 m/kg), and 72.01 ± 0.94%, respectively. The results were superior to those achieved with sludge treated solely by H2SO4 acidification or [C4mim][CF3SO3] alone. The biomacromolecules within the sludge flocs were dissolved by [C4mim][CF3SO3], while simultaneously, the microorganisms were inactivated. Consequently, the colloidal-like structures of the sludge flocs were destroyed. Additionally, the ionizable functional groups of the biomacromolecules were instantly protonated by the introduced H+ ions, and their negative charges were neutralized during the H2SO4 acidification process. The presence of H+ ions promoted the weakening of electrostatic repulsion between the sludge flocs. As a result, an enhancement of sludge dewaterability was obtained after treatment with [C4mim][CF3SO3] and H2SO4 acidification. The finding of the intensification mechanism of sludge dewaterability brought by hybrid treatment of acidification and [C4mim][CF3SO3] provides novel insights into the field of sludge disposal.

SERT-StructNet: Protein secondary structure prediction method based on multi-factor hybrid deep model.

Protein secondary structure prediction (PSSP) is a pivotal research endeavour that plays a crucial role in the comprehensive elucidation of protein functions and properties. Current prediction methodologies are focused on deep-learning techniques, particularly focusing on multi-factor features. Diverging from existing approaches, in this study, we placed special emphasis on the effects of amino acid properties and protein secondary structure propensity scores (SSPs) on secondary structure during the meticulous selection of multi-factor features. This differential feature-selection strategy results in a distinctive and effective amalgamation of the sequence and property features. To harness these multi-factor features optimally, we introduced a hybrid deep feature extraction model. The model initially employs mechanisms such as dilated convolution (D-Conv) and a channel attention network (SENet) for local feature extraction and targeted channel enhancement. Subsequently, a combination of recurrent neural network variants (BiGRU and BiLSTM), along with a transformer module, was employed to achieve global bidirectional information consideration and feature enhancement. This approach to multi-factor feature input and multi-level feature processing enabled a comprehensive exploration of intricate associations among amino acid residues in protein sequences, yielding a Q3 accuracy of 84.9% and an Sov score of 85.1%. The overall performance surpasses that of the comparable methods. This study introduces a novel and efficient method for determining the PSSP domain, which is poised to deepen our understanding of the practical applications of protein molecular structures.

Excited-state Engineering Enables Efficient Deep-blue Light-emitting Diodes Exhibiting BT.2020 Color Gamut.

Organic luminescent materials that exhibit thermally activated delayed fluorescence (TADF) can convert non-emissive triplet excitons into emissive singlet states through a reverse intersystem crossing (RISC) process. Therefore, they have tremendous potential for applications in organic light-emitting diodes (OLEDs). However, with the development of ultra-high definition 4K/8K display technologies, designing efficient deep-blue TADF materials to achieve the Commission Internationale de l'Éclairage (CIE) coordinates fulfilling BT.2020 remains a significant challenge. Here, we propose an effective approach to design deep-blue TADF molecules based on hybrid long- and short-range charge-transfer by incorporation of multiple donor moieties into organoboron multiple resonance acceptors. The resulting TADF molecule exhibits deep-blue emission at 414 nm with a full width at half maximum (FWHM) of 29 nm, together with a thousand-fold increase in RISC rate. OLEDs based on our champion material achieved a record maximum external quantum efficiency (EQE) of 22.8% with CIE coordinates of (0.163, 0.046), approaching the coordinates of the BT.2020 blue standard. Moreover, TADF-assisted fluorescence devices employing our designed material as a sensitizer exhibited an exceptional EQE of 33.1%. Our work thus provides a blueprint for future development of efficient deep-blue TADF emitters, representing an important milestone towards meeting the blue color gamut standard of BT.2020. This article is protected by copyright. All rights reserved.

Hybrid WT-CNN-GRU-based model for the estimation of reservoir water quality variables considering spatio-temporal features.

Water quality indicators (WQIs), such as chlorophyll-a (Chl-a) and dissolved oxygen (DO), are crucial for understanding and assessing the health of aquatic ecosystems. Precise prediction of these indicators is fundamental for the efficient administration of rivers, lakes, and reservoirs. This research utilized two unique DL algorithms-namely, convolutional neural network (CNNs) and gated recurrent units (GRUs)-alongside their amalgamation, CNN-GRU, to precisely gauge the concentration of these indicators within a reservoir. Moreover, to optimize the outcomes of the developed hybrid model, we considered the impact of a decomposition technique, specifically the wavelet transform (WT). In addition to these efforts, we created two distinct machine learning (ML) algorithms-namely, random forest (RF) and support vector regression (SVR)-to demonstrate the superior performance of deep learning algorithms over individual ML ones. We initially gathered WQIs from diverse locations and varying depths within the reservoir using an AAQ-RINKO device in the study area to achieve this. It is important to highlight that, despite utilizing diverse data-driven models in water quality estimation, a significant gap persists in the existing literature regarding implementing a comprehensive hybrid algorithm. This algorithm integrates the wavelet transform, convolutional neural network (CNN), and gated recurrent unit (GRU) methodologies to estimate WQIs accurately within a spatiotemporal framework. Subsequently, the effectiveness of the models that were developed was assessed utilizing various statistical metrics, encompassing the correlation coefficient (r), root mean square error (RMSE), mean absolute error (MAE), and Nash-Sutcliffe efficiency (NSE) throughout both the training and testing phases. The findings demonstrated that the WT-CNN-GRU model exhibited better performance in comparison with the other algorithms by 13% (SVR), 13% (RF), 9% (CNN), and 8% (GRU) when R-squared and DO were considered as evaluation indices and WQIs, respectively.

Wind Aggregation Enhanced Triboelectric-Electromagnetic Hybrid Generator with Slit Effect.

It is of great significance to establish a low-cost, high-efficiency, self-powered micrometeorological monitoring system for agriculture, animal husbandry, and transportation. However, each additional detection element in the meteorological monitoring system increases the power consumption of the whole system by about 0.7 W. As a renewable energy technology, a triboelectric nanogenerator has the advantages of low price and self-powered sensing. To reduce the power consumption of the micrometeorological monitoring system, this work introduces an innovative solution: the wind-gathering enhanced triboelectric-electromagnetic hybrid generator (WGE-TEHG). Coupling the thin-film vibrating triboelectric nanogenerator (TENG) and electromagnetic generator (EMG), the TENG is used to monitor wind direction and the EMG is used to monitor wind speed and provide energy needed by the system. In particular, the TENG can be used as a self-powered sensor to reduce the power consumption of the sensing system. Besides, the TENG is used to produce slit effect to enhance the output performance of EMG. The experimental results show that the WGE-TEHG can build a self-powered natural environment micrometeorological sensing system. It can monitor the wind direction, wind speed, temperature, and relative humidity. This research has great application value for the self-powered sensing implementation of a hybrid TENG and EMG.

Automated Insulin Delivery in Adults With Type 1 Diabetes and Suboptimal HbA1c During Prior Use of Insulin Pump and Continuous Glucose Monitoring: A Randomized Controlled Trial.

BACKGROUND: Automated insulin delivery (AID) systems offer promise in improving glycemic outcomes for individuals with type 1 diabetes. However, data on those who struggle with suboptimal glycemic levels despite insulin pump and continuous glucose monitoring (CGM) are limited. We conducted a randomized controlled trial to assess the effects of an AID system in this population. METHODS: Participants with hemoglobin A1c (HbA1c) ≥ 58 mmol/mol (7.5%) were allocated 1:1 to 14 weeks of treatment with the MiniMed 780G system (AID) or continuation of usual care (UC). The primary endpoint was change in time in range (TIR: 3·9-10·0 mmol/L) from baseline to week 14. After this trial period, the UC group switched to AID treatment while the AID group continued using the system. Both groups were monitored for a total of 28 weeks. RESULTS: Forty adults (mean ± SD: age 52 ± 11 years, HbA1c 67 ± 7 mmol/mol [8.3% ± 0.6%], diabetes duration 29 ±13 years) were included. After 14 weeks, TIR increased by 18.7% (95% confidence interval [CI] = 14.5, 22.9%) in the AID group and remained unchanged in the UC group (P < .0001). Hemoglobin A1c decreased by 10.0 mmol/mol (95% CI = 7.0, 13.0 mmol/mol) (0.9% [95% CI = 0.6%, 1.2%]) in the AID group but remained unchanged in the UC group (P < .0001). The glycemic benefits of AID treatment were reproduced after the 14-week extension phase. There were no episodes of severe hypoglycemia or diabetic ketoacidosis during the study. CONCLUSIONS: For adults with type 1 diabetes not meeting glycemic targets despite use of insulin pump and CGM, transitioning to an AID system confers considerable glycemic benefits.

Novel multifunctional tacrine-donepezil hybrids against Alzheimer's disease: Design synthesis and bioactivity studies.

A series of tacrine-donepezil hybrids were synthesized as potential multifunctional anti-Alzheimer's disease (AD) compounds. For this purpose, tacrine and the benzylpiperidine moiety of donepezil were fused with a hydrazone group to achieve a small library of tacrine-donepezil hybrids. In agreement with the design, all compounds showed inhibitory activity toward both acetylcholinesterase (AChE) and butyrylcholinesterase (BChE) with IC50 values in the low micromolar range. Kinetic studies on the most potent cholinesterase (ChE) inhibitors within the series showed a mixed-type inhibition mechanism on both enzymes. Also, the docking studies indicated that the compounds inhibit ChEs by dual binding site (DBS) interactions. Notably, tacrine-donepezil hybrids also exhibited significant neuroprotection against H2O2-induced cell death in a differentiated human neuroblastoma (SH-SY5Y) cell line at concentrations close to their IC50 values on ChEs and showed high to medium blood-brain barrier (BBB) permeability on human cerebral microvascular endothelial cells (HBEC-5i). Besides, the compounds do not cause remarkable toxicity in a human hepatocellular carcinoma cell line (HepG2) and SH-SY5Y cells. Additionally, the compounds were predicted to also have good bioavailability. Among the tested compounds, H4, H16, H17, and H24 stand out with their biological profile. Taken together, the proposed novel tacrine-donepezil scaffold represents a promising starting point for the development of novel anti-ChE multifunctional agents against AD.