Proton pump inhibitors may increase the risk of cisplatin-induced acute kidney injury in patients with nasopharyngeal carcinoma: a prospective cohort study.

Cisplatin is the most commonly used platinum-based treatment for nasopharyngeal carcinoma (NPC). However, its clinical application is limited owing to its nephrotoxicity and gastrointestinal reactions. Proton pump inhibitors (PPIs) have been reported to increase nephrotoxicity risk in previous studies. We aimed to evaluate whether PPIs increase cisplatin-induced nephrotoxicity in patients with NPC. In total, 295 patients were included in this prospective cohort study: 145 in the PPIs group and 150 in the non-PPIs group. All patients underwent cisplatin-based induction chemotherapy, followed by cisplatin-based concurrent chemoradiotherapy. The PPIs group received 40 mg of intravenous esomeprazole sodium for 7 days in each chemotherapy cycle. Chi-squared test and logistic regression analyses with odds ratios and 95% confidence intervals were applied to assess the association between PPIs and the risk of acute kidney injury (AKI). AKI incidence in the PPIs group was significantly higher than that in the non-PPIs group (P = 0.005). After adjusting for various confounders including demographic features, clinical features, and renal function indices, PPIs use was significantly associated with a higher AKI risk (odds ratio: 2.775; 95% confidence interval 1.280-6.020; P = 0.010). The incidences of acute and chronic kidney diseases were similar between both groups (P > 0.05), whereas the incidence of nausea was lower in the PPIs group than in the non-PPIs group (P = 0.029). This study has shown that PPIs use may increase the risk of cisplatin-induced acute nephrotoxicity in patients with NPC.

Inverse association between serum chloride levels and the risk of atrial fibrillation in chronic kidney disease patients.

Background: Electrolyte abnormalities are common symptoms of chronic kidney disease (CKD), but previous studies have mainly focussed on serum potassium and sodium levels. Chloride is an important biomarker for the prognosis of various diseases. However, the relationship between serum chloride levels and atrial fibrillation (AF) in CKD patients is unclear. Objective: In this study, we sought to determine the association between serum chloride homeostasis and AF in CKD patients. Methods: In this retrospective cohort study, we included patients who met the diagnostic criteria for CKD in China between 2000 and 2021. Competing risk regression for AF was performed. The associations of the baseline serum chloride concentration with heart failure (HF) and stroke incidence were also calculated by competing risk regression. The association of baseline serum chloride levels with all-cause death was determined by a Cox regression model. Results: The study cohort comprised 20 550 participants. During a median follow-up of 350 days (interquartile range, 123-730 days), 211 of the 20 550 CKD patients developed AF. After multivariable adjustment, every decrease in the standard deviation of serum chloride (5.02 mmol/l) was associated with a high risk for AF [sub-hazard ratio (sHR) 0.78, 95% confidence interval (CI) 0.65-0.94, P = .008]. These results were also consistent with those of the stratified and sensitivity analyses. According to the fully adjusted models, the serum chloride concentration was also associated with a high risk for incident HF (sHR 0.85, 95% CI 0.80-0.91, P < .001), a high risk for incident stroke (sHR 0.87, 95% CI 0.81-0.94, P < .001), and a high risk for all-cause death [hazard ratio (HR) 0.82, 95% CI 0.73-0.91, P < .001]. Conclusion: In this CKD population, serum chloride levels were independently and inversely associated with the incidence of AF. Lower serum chloride levels were also associated with an increased risk of incident HF, stroke, and all-cause death.

Accurate and rapid mercury susceptibility detection in aquatic samples using fluorescent probe integrated rhodamine with pyridyl isothiocyanate.

Mercury, one of the various harmful metals, is particularly significant in affecting aquatic organisms, currently gaining more attentions and sparking discussions. In response to the limitations of traditional detections, fluorescent probes have emerged as a promising solution with some advantages, such as weaker background interference, shorter processing time, higher accuracy. Thus, a novel fluorescent probe, FS-Hg-1, has been developed for assessing mercury ion (Hg2+) concentrations in aquatic products. This probe displays specific recognition of mercury ions in fluorescence spectra. Notably, FS-Hg-1 exhibits a distinct color change to pink when combined with Hg2+ (with a 948-fold increase in absorption at 568 nm) and a substantial fluorescence change towards Hg2+ (361-fold increase, excitation at 562 nm, emission at 594 nm) in N, N-dimethylformamide. The probe boasts a detection limit of 0.14 µM and rapid reaction with Hg2+ within 10 s, showing an excellent linear correlation with [Hg2+] in the range of 0 to 10 µM. Through thorough analysis using FS-Hg-1, the results align with those from the standard method (P > 0.05), with spiked recovery rates ranging from 108.4% to 113.2%. With its precise recognition, low detection limit, and remarkable sensitivity, this fluorescent assay proves effective in mercury concentration determination in aquatic samples without interference. The potential of FS-Hg-1 is promising for speedy detection of residual Hg2+ and holds significance in ensuring food safety.

Predicting time-of-flight with Cerenkov light in BGO: a three-stage network approach with multiple timing kernels prior.

In the quest for enhanced image quality in positron emission tomography (PET) reconstruction, the introduction of Time-of-Flight (TOF) constraints in TOF-PET reconstruction offers superior signal-to-noise ratio (SNR). By employing BGO detectors capable of simultaneously emitting prompt Cerenkov light and scintillation light, this approach combines the high time resolution of prompt photons with the high energy resolution of scintillation light, thereby presenting a promising avenue for acquiring more precise TOF information. In Stage One, we train a raw method capable of predicting TOF information based on coincidence waveform pairs. In Stage Two, the data is categorized into 25 classes based on signal rise time, and the pre-trained raw method is utilized to obtain TOF kernels for each of the 25 classes, thereby generating prior knowledge. Within Stage Three, our proposed Deep Learning (DL) module, combined with a bias fine-tuning module, utilizes the kernel prior to provide bias compensation values for the data, thereby refining the first-stage outputs and obtaining more accurate TOF predictions.The three-stage network built upon the LED method resulted in improvements of 11.7 ps and 41.8 ps for full width at half maximum (FWHM) and full width at tenth maximum (FWTM), respectively. Optimal performance was achieved with FWHM of 128.2 ps and FWTM of 286.6 ps when CNN and Transformer were utilized in Stages One and Three, respectively. Further enhancements of 2.3 ps and 3.5 ps for FWHM and FWTM were attained through data augmentation methods. This study employs neural networks to compensate for the timing delays in mixed (Cerenkov and scintillation photons) signals, combining multiple timing kernels as prior knowledge With deep learning models. This integration yields optimal predictive performance, offering a superior solution for TOF-PET research utilizing Cerenkov signals.

Inhibition of TFEB deacetylation in proximal tubular epithelial cells (TECs) promotes TFEB activation and alleviates TEC damage in diabetic kidney disease.

The inhibition of the autophagolysosomal pathway mediated by transcription factor EB (TFEB) inactivation in proximal tubular epithelial cells (TECs) is a key mechanism of TEC injury in diabetic kidney disease (DKD). Acetylation is a novel mechanism that regulates TFEB activity. However, there are currently no studies on whether the adjustment of the acetylation level of TFEB can reduce the damage of diabetic TECs. In this study, we investigated the effect of Trichostatin A (TSA), a typical deacetylase inhibitor, on TFEB activity and damage to TECs in both in vivo and in vitro models of DKD. Here, we show that TSA treatment can alleviate the pathological damage of glomeruli and renal tubules and delay the DKD progression in db/db mice, which is associated with the increased expression of TFEB and its downstream genes. In vitro studies further confirmed that TSA treatment can upregulate the acetylation level of TFEB, promote its nuclear translocation, and activate the expression of its downstream genes, thereby reducing the apoptosis level of TECs. TFEB deletion or HDAC6 knockdown in TECs can counteract the activation effect of TSA on autophagolysosomal pathway. We also found that TFEB enhances the transcription of Tfeb through binding to its promoter and promotes its own expression. Our results, thus, provide a novel therapeutic mechanism for DKD that the alleviation of TEC damage by activating the autophagic lysosomal pathway through upregulating TFEB acetylation can, thus, delay DKD progression.

The complete chloroplast genome of Pyankovia brachiata (amaranthaceae), an annual desert plant in China.

Pyankovia brachiata (Pall.) Akhani & Roalson 2007, is an annual plant belonging to the genus Pyankovia, family Amaranthaceae, which is widely distributed in the inland deserts of Northwest China. P. brachiata was previously categorized under the genus Salsola in Salsoleae and has been a long-standing topic of debate. Therefore, the complete chloroplast genome of P. brachiata must be studied to provide a theoretical reference for species classification. In this study, we sequenced P. brachiata samples and determined the species' complete chloroplast genome. The complete chloroplast genome was 149,922 bp in length, with one large single copy (LSC: 83,565 bp), one small single copy (SSC: 18,535 bp), and two inverted repeat regions (IRa and IRb, 23,911 bp each). It contains 132 genes, including 87 protein-coding, eight rRNA, and 37 tRNA genes. The phylogenetic position showed that P. brachiata has the closest relationship with Caroxylon passerinum (accession number: NC057191.1). This study will provide genetic information and be beneficial to understanding the systematic position of P. brachiata within the Amaranthaceae.

Glutaminolysis is a Potential Therapeutic Target for Kidney Diseases.

Metabolic reprogramming contributes to the progression and prognosis of various kidney diseases. Glutamine is the most abundant free amino acid in the body and participates in more metabolic processes than other amino acids. Altered glutamine metabolism is a prominent feature in different kidney diseases. Glutaminolysis converts glutamine into the TCA cycle metabolite, alpha-ketoglutarate, via a cascade of enzymatic reactions. This metabolic pathway plays pivotal roles in inflammation, maladaptive repair, cell survival and proliferation, redox homeostasis, and immune regulation. Given the crucial role of glutaminolysis in bioenergetics and anaplerotic fluxes in kidney pathogenesis, studies on this cascade could provide a better understanding of kidney diseases, thus inspiring the development of potential methods for targeted therapy. Emerging evidence has shown that targeting glutaminolysis is a promising therapeutic strategy for ameliorating kidney disease. In this narrative review, equation including keywords related to glutamine, glutaminolysis and kidney are subjected to an exhaustive search on Pubmed database, we identified all relevant articles published before 1 April, 2024. Afterwards, we summarize the regulation of glutaminolysis in major kidney diseases and its underlying molecular mechanisms. Furthermore, we highlight therapeutic strategies targeting glutaminolysis and their potential clinical applications.

Selenium nanoparticles alleviate renal ischemia/reperfusion injury by inhibiting ferritinophagy via the XBP1/NCOA4 pathway.

Acute kidney injury (AKI) is closely related to lysosomal dysfunction and ferroptosis in renal tubular epithelial cells (TECs), for which effective treatments are urgently needed. Although selenium nanoparticles (SeNPs) have emerged as promising candidates for AKI therapy, their underlying mechanisms have not been fully elucidated. Here, we investigated the effect of SeNPs on hypoxia/reoxygenation (H/R)-induced ferroptosis and lysosomal dysfunction in TECs in vitro and evaluated their efficacy in a murine model of ischemia/reperfusion (I/R)-AKI. We observed that H/R-induced ferroptosis was accompanied by lysosomal Fe2+ accumulation and dysfunction in TECs, which was ameliorated by SeNPs administration. Furthermore, SeNPs protected C57BL/6 mice against I/R-induced inflammation and ferroptosis. Mechanistically, we found that lysosomal Fe2+ accumulation and ferroptosis were associated with the excessive activation of NCOA4-mediated ferritinophagy, a process mitigated by SeNPs through the upregulation of X-box binding protein 1 (XBP1). Downregulation of XBP1 promoted ferritinophagy and partially counteracted the protective effects of SeNPs on ferroptosis inhibition in TECs. Overall, our findings revealed a novel role for SeNPs in modulating ferritinophagy, thereby improving lysosomal function and attenuating ferroptosis of TECs in I/R-AKI. These results provide evidence for the potential application of SeNPs as therapeutic agents for the prevention and treatment of AKI.

IE-CycleGAN: improved cycle consistent adversarial network for unpaired PET image enhancement.

PURPOSE: Technological advances in instruments have greatly promoted the development of positron emission tomography (PET) scanners. State-of-the-art PET scanners such as uEXPLORER can collect PET images of significantly higher quality. However, these scanners are not currently available in most local hospitals due to the high cost of manufacturing and maintenance. Our study aims to convert low-quality PET images acquired by common PET scanners into images of comparable quality to those obtained by state-of-the-art scanners without the need for paired low- and high-quality PET images. METHODS: In this paper, we proposed an improved CycleGAN (IE-CycleGAN) model for unpaired PET image enhancement. The proposed method is based on CycleGAN, and the correlation coefficient loss and patient-specific prior loss were added to constrain the structure of the generated images. Furthermore, we defined a normalX-to-advanced training strategy to enhance the generalization ability of the network. The proposed method was validated on unpaired uEXPLORER datasets and Biograph Vision local hospital datasets. RESULTS: For the uEXPLORER dataset, the proposed method achieved better results than non-local mean filtering (NLM), block-matching and 3D filtering (BM3D), and deep image prior (DIP), which are comparable to Unet (supervised) and CycleGAN (supervised). For the Biograph Vision local hospital datasets, the proposed method achieved higher contrast-to-noise ratios (CNR) and tumor-to-background SUVmax ratios (TBR) than NLM, BM3D, and DIP. In addition, the proposed method showed higher contrast, SUVmax, and TBR than Unet (supervised) and CycleGAN (supervised) when applied to images from different scanners. CONCLUSION: The proposed unpaired PET image enhancement method outperforms NLM, BM3D, and DIP. Moreover, it performs better than the Unet (supervised) and CycleGAN (supervised) when implemented on local hospital datasets, which demonstrates its excellent generalization ability.

Real-world effectiveness of early insulin therapy on the incidence of cardiovascular events in newly diagnosed type 2 diabetes.

Early insulin therapy is capable to achieve glycemic control and restore ß-cell function in newly diagnosed type 2 diabetes (T2D), but its effect on cardiovascular outcomes in these patients remains unclear. In this nationwide real-world study, we analyzed electronic health record data from 19 medical centers across China between 1 January 2000, and 26 May 2022. We included 5424 eligible patients (mean age 56 years, 2176 women/3248 men) who were diagnosed T2D within six months and did not have prior cardiovascular disease. Multivariable Cox regression models were used to estimate the associations of early insulin therapy (defined as the first-line therapy for at least two weeks in newly diagnosed T2D patients) with the incidence of major cardiovascular events including coronary heart disease (CHD), stroke, and hospitalization for heart failure (HF). During 17,158 persons years of observation, we documented 834 incident CHD cases, 719 stroke cases, and 230 hospitalized cases for HF. Newly diagnosed T2D patients who received early insulin therapy, compared with those who did not receive such treatment, had 31% lower risk of incident stroke, and 28% lower risk of hospitalization for HF. No significant difference in the risk of CHD was observed. We found similar results when repeating the aforesaid analysis in a propensity-score matched population of 4578 patients and with inverse probability of treatment weighting models. These findings suggest that early insulin therapy in newly diagnosed T2D may have cardiovascular benefits by reducing the risk of incident stroke and hospitalization for HF.

Neural network aided extended Kalman filtering for inverse imaging of cardiac transmembrane potential.

Objective.The aim of this study is to address the limitations in reconstructing the electrical activity of the heart from the body surface electrocardiogram, which is an ill-posed inverse problem. Current methods often assume values commonly used in the literature in the absence ofa prioriknowledge, leading to errors in the model. Furthermore, most methods ignore the dynamic activation process inherent in cardiomyocytes during the cardiac cycle.Approach.To overcome these limitations, we propose an extended Kalman filter (EKF)-based neural network approach to dynamically reconstruct cardiac transmembrane potential (TMP). Specifically, a recurrent neural network is used to establish the state estimation equation of the EKF, while a convolutional neural network is used as the measurement equation. The Jacobi matrix of the network undergoes a correction feedback process to obtain the Kalman gain.Main results.After repeated iterations, the final estimated state vector, i.e. the reconstructed image of the TMP, is obtained. The results from both the final simulation and real experiments demonstrate the robustness and accurate quantification of the model.Significance.This study presents a new approach to cardiac TMP reconstruction that offers higher accuracy and robustness compared to traditional methods. The use of neural networks and EKFs allows dynamic modelling that takes into account the activation processes inherent in cardiomyocytes and does not requirea prioriknowledge of inputs such as forward transition matrices.

Research Progress of Drug Delivery Systems Targeting the Kidneys.

Chronic kidney disease (CKD) affects more than 10% of the global population, and its incidence is increasing, partially due to an increase in the prevalence of disease risk factors. Acute kidney injury (AKI) is an independent risk factor for CKD and end-stage renal disease (ESRD). The pathogenic mechanisms of CKD provide several potential targets for its treatment. However, due to off-target effects, conventional drugs for CKD typically require high doses to achieve adequate therapeutic effects, leading to long-term organ toxicity. Therefore, ideal treatments that completely cure the different types of kidney disease are rarely available. Several approaches for the drug targeting of the kidneys have been explored in drug delivery system research. Nanotechnology-based drug delivery systems have multiple merits, including good biocompatibility, suitable degradability, the ability to target lesion sites, and fewer non-specific systemic effects. In this review, the development, potential, and limitations of low-molecular-weight protein-lysozymes, polymer nanomaterials, and lipid-based nanocarriers as drug delivery platforms for treating AKI and CKD are summarized.

Sterile inflammation of peritoneal membrane caused by peritoneal dialysis: focus on the communication between immune cells and peritoneal stroma.

Peritoneal dialysis is a widely used method for treating kidney failure. However, over time, the peritoneal structure and function can deteriorate, leading to the failure of this therapy. This deterioration is primarily caused by infectious and sterile inflammation. Sterile inflammation, which is inflammation without infection, is particularly concerning as it can be subtle and often goes unnoticed. The onset of sterile inflammation involves various pathological processes. Peritoneal cells detect signals that promote inflammation and release substances that attract immune cells from the bloodstream. These immune cells contribute to the initiation and escalation of the inflammatory response. The existing literature extensively covers the involvement of different cell types in the sterile inflammation, including mesothelial cells, fibroblasts, endothelial cells, and adipocytes, as well as immune cells such as macrophages, lymphocytes, and mast cells. These cells work together to promote the occurrence and progression of sterile inflammation, although the exact mechanisms are not fully understood. This review aims to provide a comprehensive overview of the signals from both stromal cells and components of immune system, as well as the reciprocal interactions between cellular components, during the initiation of sterile inflammation. By understanding the cellular and molecular mechanisms underlying sterile inflammation, we may potentially develop therapeutic interventions to counteract peritoneal membrane damage and restore normal function.

Transformer-CNN hybrid network for improving PET time of flight prediction.

Objective.In positron emission tomography (PET) reconstruction, the integration of time-of-flight (TOF) information, known as TOF-PET, has been a major research focus. Compared to traditional reconstruction methods, the introduction of TOF enhances the signal-to-noise ratio of images. Precision in TOF is measured by full width at half maximum (FWHM) and the offset from ground truth, referred to as coincidence time resolution (CTR) and bias.Approach.This study proposes a network combining transformer and convolutional neural network (CNN) to utilize TOF information from detector waveforms, using event waveform pairs as inputs. This approach integrates the global self-attention mechanism of Transformer, which focuses on temporal relationships, with the local receptive field of CNN. The combination of global and local information allows the network to assign greater weight to the rising edges of waveforms, thereby extracting valuable temporal information for precise TOF predictions. Experiments were conducted using lutetium yttrium oxyorthosilicate (LYSO) scintillators and silicon photomultiplier (SiPM) detectors. The network was trained and tested using the waveform datasets after cropping.Main results.Compared to the constant fraction discriminator (CFD), CNN, CNN with attention, long short-term memory (LSTM) and Transformer, our network achieved an average CTR of 189 ps, reducing it by 82 ps (more than 30%), 13 ps (6.4%), 12 ps (6.0%), 16 ps (7.8%) and 9 ps (4.6%), respectively. Additionally, a reduction of 10.3, 8.7, 6.7 and 4 ps in average bias was achieved compared to CNN, CNN with attention, LSTM and Transformer.Significance.This work demonstrates the potential of applying the Transformer for PET TOF estimation using real experimental data. Through the integration of both CNN and Transformer with local and global attention, it achieves optimal performance, thereby presenting a novel direction for future research in this field.

Three-dimensional spatio-angular fluorescence microscopy with a polarized dual-view inverted selective-plane illumination microscope (pol-diSPIM).

Polarized fluorescence microscopy is a valuable tool for measuring molecular orientations, but techniques for recovering three-dimensional orientations and positions of fluorescent ensembles are limited. We report a polarized dual-view light-sheet system for determining the three-dimensional orientations and diffraction-limited positions of ensembles of fluorescent dipoles that label biological structures, and we share a set of visualization, histogram, and profiling tools for interpreting these positions and orientations. We model our samples, their excitation, and their detection using coarse-grained representations we call orientation distribution functions (ODFs). We apply ODFs to create physics-informed models of image formation with spatio-angular point-spread and transfer functions. We use theory and experiment to conclude that light-sheet tilting is a necessary part of our design for recovering all three-dimensional orientations. We use our system to extend known two-dimensional results to three dimensions in FM1-43-labelled giant unilamellar vesicles, fast-scarlet-labelled cellulose in xylem cells, and phalloidin-labelled actin in U2OS cells. Additionally, we observe phalloidin-labelled actin in mouse fibroblasts grown on grids of labelled nanowires and identify correlations between local actin alignment and global cell-scale orientation, indicating cellular coordination across length scales.

Signal separation of simultaneous dual-tracer PET imaging based on global spatial information and channel attention.

BACKGROUND: Simultaneous dual-tracer positron emission tomography (PET) imaging efficiently provides more complete information for disease diagnosis. The signal separation has long been a challenge of dual-tracer PET imaging. To predict the single-tracer images, we proposed a separation network based on global spatial information and channel attention, and connected it to FBP-Net to form the FBPnet-Sep model. RESULTS: Experiments using simulated dynamic PET data were conducted to: (1) compare the proposed FBPnet-Sep model to Sep-FBPnet model and currently existing Multi-task CNN, (2) verify the effectiveness of modules incorporated in FBPnet-Sep model, (3) investigate the generalization of FBPnet-Sep model to low-dose data, and (4) investigate the application of FBPnet-Sep model to multiple tracer combinations with decay corrections. Compared to the Sep-FBPnet model and Multi-task CNN, the FBPnet-Sep model reconstructed single-tracer images with higher structural similarity, peak signal-to-noise ratio and lower mean squared error, and reconstructed time-activity curves with lower bias and variation in most regions. Excluding the Inception or channel attention module resulted in degraded image qualities. The FBPnet-Sep model showed acceptable performance when applied to low-dose data. Additionally, it could deal with multiple tracer combinations. The qualities of predicted images, as well as the accuracy of derived time-activity curves and macro-parameters were slightly improved by incorporating a decay correction module. CONCLUSIONS: The proposed FBPnet-Sep model was considered a potential method for the reconstruction and signal separation of simultaneous dual-tracer PET imaging.

Frequency-Enhanced Geometric-Constrained Reconstruction for Localizing Myocardial Infarction in 12-Lead Electrocardiograms.

Determining the location of myocardial infarction is crucial for clinical management and therapeutic stratagem. However, existing diagnostic tools either sacrifice ease of use or are limited by their spatial resolution. Addressing this, we aim to refine myocardial infarction localization via surface potential reconstruction of the ventricles in 12-lead electrocardiograms (ECG). A notable obstacle is the ill-posed nature of such reconstructions. To overcome this, we introduce the frequency-enhanced geometric-constrained iterative network (FGIN). FGIN begins by mining the latent features from ECG data across both time and frequency domains. Subsequently, it increases the data dimensionality of ECG and captures intricate features using convolutional layers. Finally, FGIN incorporates ventricular geometry as a constraint on surface potential distribution. It allocates variable weights to distinct edges. Experimental validation of FGIN confirms its efficacy over synthetic and clinical datasets. On the synthetic dataset, FGIN outperforms seven existing reconstruction methods, attaining the highest Pearson Correlation Coefficient of 0.8624, the lowest Root Mean Square Error of 0.1548, and the highest Structural Similarity Index Measure of 0.7988. On the clinical public dataset (2007 PhysioNet/Computers in Cardiology Challenge), FGIN achieves better localization results than other approaches, according to the clinical standard 17-segment model, achieving an average Segment Overlap of 87.2%. Clinical trials on 50 patients demonstrate FGIN's effectiveness, showing an average accuracy of 91.6% and an average Segment Overlap of 88.2%.

Dipeptidyl peptidase 4 inhibitors vs. metformin for new-onset dementia: a propensity score-matched cohort study.

BACKGROUND: Hypoglycemic pharmacotherapy interventions for alleviating the risk of dementia remains controversial, particularly about dipeptidyl peptidase 4 (DPP4) inhibitors versus metformin. Our objective was to investigate whether the initiation of DPP4 inhibitors, as opposed to metformin, was linked to a reduced risk of dementia. METHODS: We included individuals with type 2 diabetes over 40 years old who were new users of DPP4 inhibitors or metformin in the Chinese Renal Disease Data System (CRDS) database between 2009 and 2020. The study employed Kaplan-Meier and Cox regression for survival analysis and the Fine and Gray model for the competing risk of death. RESULTS: Following a 1:1 propensity score matching, the analysis included 3626 DPP4 inhibitor new users and an equal number of metformin new users. After adjusting for potential confounders, the utilization of DPP4 inhibitors was associated with a decreased risk of all-cause dementia compared to metformin (hazard ratio (HR) 0.63, 95% confidence interval (CI) 0.45-0.89). Subgroup analysis revealed that the utilization of DPP4 inhibitors was associated with a reduced incidence of dementia in individuals who initiated drug therapy at the age of 60 years or older (HR 0.69, 95% CI 0.48-0.98), those without baseline macrovascular complications (HR 0.62, 95% CI 0.41-0.96), and those without baseline microvascular complications (HR 0.67, 95% CI 0.47-0.98). CONCLUSION: In this real-world study, we found that DPP4 inhibitors presented an association with a lower risk of dementia in individuals with type 2 diabetes than metformin, particularly in older people and those without diabetes-related comorbidities.

Micro-Electro-Mechanical Systems Microphones: A Brief Review Emphasizing Recent Advances in Audible Spectrum Applications.

The MEMS microphone is a representative device among the MEMS family, which has attracted substantial research interest, and those tailored for human voice have earned distinct success in commercialization. Although sustained development persists, challenges such as residual stress, environmental noise, and structural innovation are posed. To collect and summarize the recent advances in this subject, this paper presents a concise review concerning the transduction mechanism, diverse mechanical structure topologies, and effective methods of noise reduction for high-performance MEMS microphones with a dynamic range akin to the audible spectrum, aiming to provide a comprehensive and adequate analysis of this scope.