Detection Bias Research Articles

Scanning Photocurrent Microscopy in Single Crystal Multidimensional Hybrid Lead Bromide Perovskites.

We investigated solution-grown single crystals of multidimensional 2D-3D hybrid lead bromide perovskites using spatially resolved photocurrent and photoluminescence. Scanning photocurrent microscopy (SPCM) measurements where the electrodes consisted of a dip probe contact and a back contact. The crystals revealed significant differences between 3D and multidimensional 2D-3D perovskites under biased detection, not only in terms of photocarrier decay length values but also in the spatial dynamics across the crystal. In general, the photocurrent maps indicate that the closer the border proximity, the shorter the effective decay length, thus suggesting a determinant role of the border recombination centers in monocrystalline samples. In this case, multidimensional 2D-3D perovskites exhibited a simple fitting model consisting of a single exponential, while 3D perovskites demonstrated two distinct charge carrier migration dynamics within the crystal: fast and slow. Although the first one matches that of the 2D-3D perovskite, the long decay of the 3D sample exhibits a value two orders of magnitude larger. This difference could be attributed to the presence of interlayer screening and a larger exciton binding energy of the multidimensional 2D-3D perovskites with respect to their 3D counterparts.

Evaluating the Causal Association between Inflammatory Bowel Disease and Risk of Atherosclerotic Cardiovascular Disease: Univariable and Multivariable Mendelian Randomization Study.

Observational studies suggested that inflammatory bowel disease (IBD) (i.e., Crohn's disease [CD] and ulcerative colitis [UC]) is associated with an increased risk of atherosclerotic cardiovascular disease (ASCVD), including coronary artery disease (CAD) and ischemic stroke. However, it is still unclear whether the observed associations causally exist. Thus, we aim to examine the potential effect of IBD, CD, and UC on the risk of CAD and ischemic stroke, using a two-sample Mendelian randomization (MR) study. Genetic instruments for IBD, CD, and UC were retrieved from the latest published genome-wide association studies (GWASs) of European ancestry. GWAS summary data for instrument-outcome associations were gathered from four independent resources: CARDIoGRAMplusC4D Consortium, MEGASTROKE consortium, FinnGen, and UK Biobank. The inverse variance weighted (IVW) method and multiple pleiotropy-robust approaches were conducted and, subsequently, combined in a fixed-effect meta-analysis. Moreover, multivariable MR (MVMR) analysis was conducted to adjust for potential influencing instrumental variables. The IVW method revealed no causal effect of IBD on the risk of CAD (overall IBD on CAD: OR 1.003, 95%CI 0.982 to 1.025; CD on CAD: OR 0.997, 95%CI 0.978 to 1.016; UC on CAD: OR 0.986, 95%CI 0.963 to 1.010) or the risk of ischemic stroke (overall IBD on ischemic stroke: OR 0.994, 95%CI 0.970 to 1.018; CD on ischemic stroke: OR 0.996, 95%CI 0.979 to 1.014; UC on ischemic stroke: OR 0.999, 95%CI 0.978 to 1.020). The results of the meta-analysis and MVMR remained consistent. Our MR analysis does not support a causal effect of IBD on CAD and ischemic stroke, and previous results from observational studies might be biased through uncontrolled confoundings (such as IBD-specific medications and detection bias, etc.) that warrant further research.

Large Scale Limit of the Observed Galaxy Power Spectrum.

The large scale limit of the galaxy power spectrum provides a unique window into the early Universe through a possible detection of scale dependent bias produced by primordial non Gaussianities. On such large scales, relativistic effects could become important and be confused for a primordial signal. In this Letter we provide the first consistent estimate of such effects in the observed galaxy power spectrum, and discuss their possible degeneracy with local primordial non Gaussianities. We also clarify the physical differences between the two signatures, as revealed by their different sensitivity to the large scale gravitational potential. Our results indicate that, while relativistic effects could easily account for 10% of the observed power spectrum, the subset of those with a similar scale dependence to a primordial signal can be safely ignored for current galaxy surveys, but it will become relevant for future observational programs.

Front-end AI vs. Back-end AI: new framework for securing truth in communication during the generative AI era

The proliferation of artificial intelligence (AI) in digital platforms has complicated the concept of truth in communication studies. The article presents the dichotomic framework of Front-end AI and Back-end AI to tackle the complexity of distinguishing truth. Front-end AI refers to AI technology used up-front, often as the face of a product or service, challenging the authenticity and truthfulness of content. In contrast, Back-end AI refers to AI technology used behind the scenes, which can generate misleading or biased content without disclosing its AI-generated nature. Addressing these challenges requires different approaches, such as verification and ethical guidelines for Front-end AI and algorithmic transparency, bias detection, and human oversight for Back-end AI.

Degradation of methylation signals in cryopreserved DNA

BackgroundBlood-based DNA methylation has shown great promise as a biomarker in a wide variety of diseases. Studies of DNA methylation in blood often utilize samples which have been cryopreserved for years or even decades. Therefore, changes in DNA methylation associated with long-term cryopreservation can introduce biases or otherwise mislead methylation analyses of cryopreserved DNA. However, previous studies have presented conflicting results with studies reporting hypomethylation, no effect, or even hypermethylation of DNA following long-term cryopreservation. These studies may have been limited by insufficient sample sizes, or by their profiling of methylation only on an aggregate global scale, or profiling of only a few CpGs.ResultsWe analyzed two large prospective cohorts: a discovery (n = 126) and a validation (n = 136) cohort, where DNA was cryopreserved for up to four years. In both cohorts there was no detectable change in mean global methylation across increasing storage durations as DNA. However, when analysis was performed on the level of individual CpG methylation both cohorts exhibited a greater number of hypomethylated than hypermethylated CpGs at q-value < 0.05 (4049 hypomethylated but only 50 hypermethylated CpGs in discovery, and 63 hypomethylated but only 6 hypermethylated CpGs in validation). The results were the same even after controlling for age, storage duration as buffy coat prior to DNA extraction, and estimated cell type composition. Furthermore, we find that in both cohorts, CpGs have a greater likelihood to be hypomethylated the closer they are to a CpG island; except for CpGs at the CpG islands themselves which are less likely to be hypomethylated.ConclusionCryopreservation of DNA after a few years results in a detectable bias toward hypomethylation at the level of individual CpG methylation, though when analyzed in aggregate there is no detectable change in mean global methylation. Studies profiling methylation in cryopreserved DNA should be mindful of this hypomethylation bias, and more attention should be directed at developing more stable methods of DNA cryopreservation for biomedical research or clinical use.

Rapid quantitative analysis of Rongalite adulteration in rice flour using autoencoder and residual-based model associated with portable Raman spectroscopy

Rice flour is a raw material for various foods and is used as a substitute for wheat flour. However, some merchants adulterate rice flour with the illegal additive Rongalite to extend the shelf life and earn illegal profits. Rongalite is highly carcinogenic, and ingestion of more than 10 g can even cause death. high-performance liquid chromatography (HPLC) and mass spectrometry (MS) are currently the main methods for detecting food adulteration, however, the existing methods have many limitations, complex operation, expensive instrumentation, etc. Raman spectroscopy has the advantages of convenience and non-destructive samples, but Raman spectroscopy can be affected by interference such as fluorescence background that affects detection, in addition to the problem of difficult quantitative analysis due to nonlinear bias. In this article, we used the preprocessing method of Savitzky-Golay smoothing filtering and VTPspline to improve the quality of the spectra and proposed the SARNet, which combines autoencoder and residual network to achieve the quantitative analysis of Rongalite content in rice flour. The new model combines a linear model with a nonlinear model, which can solve the nonlinear problem effectively. Experiments showed that the new SARNet model achieved state-of-the-art results, achieving the best R2 of 0.9703 and RMSEP of 0.0075. The lowest Rongalite concentration detected by the portable Raman spectrometer was 0.49%. In summary, the proposed method using portable Raman spectroscopy combined with machine learning has low detection bias and high accuracy, which can realize quantitative analyses of adulterated Rongalite in rice flour quickly. The method provides an accurate and nondestructive analytical tool in the field of food detection.

Implications for Validation of IMERG Satellite Precipitation in a Complex Mountainous Region

Satellite-based precipitation retrievals such as the Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG), provide alternative data in mountainous regions. In this study, we evaluated IMERG in the Yarlung Tsangbo Grand Canyon (YGC) using ground observations. It was found that IMERG underestimated the total rainfall primarily due to under-detection of rainfall events, with misses being more prevalent than false alarms. We analyzed the relationships between the probability of detection (POD), false alarm ratio (FAR), bias in detection (BID), and Heidke skill score (HSS) and terrain factors. It was found that the POD decreased with elevation, leading to increased underestimation of rainfall events at higher elevations, and the FAR was higher in valley sites. In terms of the hit events, IMERG overestimated the light rainfall events and underestimated the heavy rainfall events and the negative bias in the hit events decreased with elevation. IMERG could capture the early morning peak precipitation in the YGC region but underestimated the amplitude of the diurnal variation. This bias was inherent at the sensor level, and the Global Precipitation Climatology Center (GPCC) calibration partially improved the underestimation. However, this improvement was not sufficient for the YGC region. This study fills the gap in IMERG validation in a complex mountainous region and has implications for users and developers.

Studying bias in visual features through the lens of optimal transport

Computer vision systems are employed in a variety of high-impact applications. However, making them trustworthy requires methods for the detection of potential biases in their training data, before models learn to harm already disadvantaged groups in downstream applications. Image data are typically represented via extracted features, which can be hand-crafted or pre-trained neural network embeddings. In this work, we introduce a framework for bias discovery given such features that is based on optimal transport theory; it uses the (quadratic) Wasserstein distance to quantify disparity between the feature distributions of two demographic groups (e.g., women vs men). In this context, we show that the Kantorovich potentials of the images, which are a byproduct of computing the Wasserstein distance and act as “transportation prices", can serve as bias scores by indicating which images might exhibit distinct biased characteristics. We thus introduce a visual dataset exploration pipeline that helps auditors identify common characteristics across high- or low-scored images as potential sources of bias. We conduct a case study to identify prospective gender biases and demonstrate theoretically-derived properties with experiments on the CelebA and Biased MNIST datasets.

Two super-Earths at the edge of the habitable zone of the nearby M dwarf TOI-2095

The main scientific goal of TESS is to find planets smaller than Neptune around stars that are bright enough to allow for further characterization studies. Given our current instrumentation and detection biases, M dwarfs are prime targets in the search for small planets that are in (or near) the habitable zone of their host star. In this work, we use photometric observations and CARMENES radial velocity (RV) measurements to validate a pair of transiting planet candidates found by TESS. The data were fitted simultaneously, using a Bayesian Markov chain Monte Carlo (MCMC) procedure and taking into account the stellar variability present in the photometric and spectroscopic time series. We confirm the planetary origin of the two transiting candidates orbiting around TOI-2095 (LSPM J1902+7525). The star is a nearby M dwarf (d = 41.90 ± 0.03 pc, Teff = 3759 ± 87 K, V = 12.6 mag), with a stellar mass and radius of M* = 0.44 ± 0.02 M⊙ and R* = 0.44 ± 0.02 R⊙, respectively. The planetary system is composed of two transiting planets: TOI-2095b, with an orbital period of Pb = 17.66484 ± (7 × 10−5) days, and TOI-2095c, with Pc = 28.17232 ± (14 × 10−5) days. Both planets have similar sizes with Rb = 1.25 ± 0.07 R⊕ and Rc = 1.33 ± 0.08 R⊕ for planet b and planet c, respectively. Although we did not detect the induced RV variations of any planet with significance, our CARMENES data allow us to set stringent upper limits on the masses of these objects. We find Mb &lt; 4.1 M⊕ for the inner and Mc &lt; 7.4 M⊕ for the outer planet (95% confidence level). These two planets present equilibrium temperatures in the range of 300–350 K and are close to the inner edge of the habitable zone of their star.

Can Cold Jupiters Sculpt the Edge-of-the-multis?

Compact systems of multiple close-in super-Earths/sub-Neptunes (compact multis) are a ubiquitous outcome of planet formation. It was recently discovered that the outer edges of compact multis are located at smaller orbital periods than expected from geometric and detection biases alone, suggesting some truncation or transition in the outer architectures. Here we test whether this edge-of-the-multis might be explained in any part by distant giant planets in the outer regions (≳1 au) of the systems. We investigate the dynamical stability of observed compact multis in the presence of hypothetical giant (≳0.5 M Jup) perturbing planets. We identify what parameters would be required for hypothetical perturbing planets if they were responsible for dynamically sculpting the outer edges of compact multis. Edge-sculpting perturbers are generally in the range of P ∼ 100–500 days for the average compact multi, with most between P ∼ 200 and 300 days. Given the relatively close separation, we explore the detectability of the hypothetical edge-sculpting perturbing planets, finding that they would be readily detectable in transit and radial velocity data. We compare to observational constraints and find it unlikely that dynamical sculpting from distant giant planets contributes significantly to the edge-of-the-multis. However, this conclusion could be strengthened in future work by a more thorough analysis of the detection yields of the perturbing planets.

Polyautoimmunity in patients with cutaneous lupus erythematosus: A nationwide sex- and age-matched cohort study from Denmark

Polyautoimmunity is defined as having 2 or more autoimmune diseases. Little is known about polyautoimmunity in patients with cutaneous lupus erythematosus (CLE). To estimate prevalence and 5-year incidence of non-lupus erythematosus (LE) autoimmune diseases in patients with CLE. Patients with CLE were identified In the Danish National Patient Registry and each patient was age- and sex-matched with 10 general population controls. Outcome information on non-LE autoimmune diseases was obtained by register-linkage between Danish National Patient Registry and the National Prescription Register. The risk ratio (RR) for prevalent non-LE autoimmune disease at time of CLE diagnosis was calculated in modified Poisson regression; and hazard ratios (HRs) for incident non-LE autoimmune disease were estimated in Cox regression analyses. Overall, 1674 patients with CLE had a higher prevalence of a non-LE autoimmune disease than the comparators (18.5 vs 7.9%; RR 2.4; 95% CI, 2.1 to 2.6). Correspondingly, the cumulative incidence of a non-LE autoimmune disease during 5years of follow-up was increased for the patients with CLE: HR 3.5(95% CI, 3.0 to 4.0). Risk of detection and misclassification bias, mainly pertaining to the CLE group. Patients with CLE had higher prevalence and 5-year cumulative incidence of a non-LE autoimmune disease than the general population.

Multi-Object Tracking Based on Re-Identification Enhancement and Associated Correction

During the Multi-Object Tracking (MOT) process, the effectiveness of Re-Identification (Re-ID) is typically contingent upon the performance of detection. In particular, detection errors may result in the blurring of Re-ID features, thereby diminishing the efficacy of Re-ID. In particular, Re-ID exhibits poor robustness in intricate scenarios such as distance and scale alterations, occlusion, and crowding. Meanwhile, the frequent occurrence of object occlusion can result in the interruption of object tracking, thereby impacting the tracking efficiency. To address these issues, a novel MOT method approach that leverages Re-ID enhancement and association correction is proposed. Through an unanchored, one-stage backbone network with CenterNet as the detector, the object position is detected by predicting the heatmap of the object center point. A constraint on the Predicted Distance Difference Loss (PDDL) is then designed for the response value of the predicted heatmap, effectively enhancing the detection accuracy and reduce the detection bias. To address the issue of Re-ID reliance on detection accuracy and enhance its robustness, an Effective Feature Graph Convolutional Neural network (EFGCN) is proposed. This approach expands learnable effective features by constructing a graph model that enhances them through a GCN model of heatmap true value locations and surrounding response value locations. Additionally, a target trajectory Association Correction Module (ACM) is introduced in the association phase to handle interruptions and trajectory fragments caused by occlusion. By improving data association for tracking judgment when dealing with occluded targets and considering the relationship between trajectory fragmentation and the detection of low-scoring targets, attempts are made to continue with tracking occluded targets to form complete object associations. The proposed method is evaluated on the MOT16 and MOT17 datasets and compared with other algorithms. The results demonstrate that it is highly effective.

Efficacy and safety of atropine at different concentrations in prevention of myopia progression in Asian children: a systematic review and Meta-analysis of randomized clinical trials.

To assess the efficacy versus the adverse effects of various concentrations of atropine in the prevention of myopia in Asian children. Databases (PubMed, EMBASE, the Cochrane Library and Web of science) were comprehensively searched from inception to April 2022. Types of studies included were randomized clinical trials (RCTs). The published languages were limited to English. Two researchers assessed the quality of included studies independently using Cochrane risk of bias tool based on the Cochrane Handbook for Systematic Reviews of Interventions. Funnel plots and Egger's test were used for detection of publication bias. Meta-analyses were conducted using STATA (version 15.0; StataCorp). A total of 15 RCTs involving 2268 patients were included in the study. In the atropine group, spherical equivalent progressed at a significantly lower rate [weighted mean difference (WMD)=0.39, 95% confidence interval (CI): 0.23, 0.54] than in the control group. A WMD of 0.15 mm was associated with less axial elongation (95%CI -0.19, -0.10). Different doses showed statistically significant differences (P<0.05) and an improved effect could result from a higher concentration. Changes in photopic pupil size and mesopic pupil size in atropine group is 0.70 mm (95%CI: 0.33, 1.06) and 0.38 mm (95%CI: 0.22, 0.54) more than the control group. In the present Meta-analysis, no changes in accommodative amplitude (AA) were associated with atropine administration. Atropine administration increased the risk of adverse effects by 1.37 times. Concentrations of less than 1% atropine are able to effectively retard diopter and axis growth of myopia in Asian children in a dose-dependent manner. Meanwhile, it caused pupil enlargement, but induced no change in the AA within this range. Further study is required to determine the dosage needed to achieve maximum efficacy and minimal side effects.

Quantifying ant diversity and community in a subalpine forest mosaic: a comparison of two methods

Sampling efficiency, composition and detection biases associated with pitfall-trap and sample plot (standardised hand-collecting) methods were compared at seven high montane sites at Lasha Mountain, Yunnan, China. Rarefaction-interpolation curves showed total species diversity and sample coverage from each method were undifferentiated. Permutational multivariate analysis of variance showed highly overlapping communities. However, of all species collected, just 53% of all species found were shared between the two methods; 30% were exclusive to sample plot samples and 17% were exclusive to pitfall traps. Modelled comparisons of species richness (alpha) and proportion of species from the population (beta diversity) showed differences between methods. Permutational multivariate analysis of variance blocked by site showed community composition differed between methods, defined largely by the presence of a few dominant species.Implications for insect conservation: Our findings suggest that results from the two methods cannot be directly compared and are imperfect substitutes to one another. For long-term monitoring of biodiversity in complex forest mosaic systems, we suggest integrating a suite of complementary methods to achieve more complete representation of ant composition and diversity.

Reporting quality and risk of bias assessment of animal research on Chaihu-Shugan-San for depression: A systematic review

ObjectiveChaihu-Shugan-San (CSS) is a traditional Chinese medicine formula employed to treat depression. We aim to conduct a reporting quality assessment and risk of bias evaluation of animal research on CSS for depression. MethodsTo acquire eligible studies, two reviewers searched plentiful databases from inception to October 23rd, 2021. Reporting quality assessment and risk of bias assessment of the included animal studies were evaluated by using Animal Research: Reporting In Vivo Experiments (ARRIVE) guidelines and the SYstematic Review Centre for Laboratory animal Experimentation (SYRCLE) risk of bias tool, respectively. ResultsThe initial search identified 720 records, while only 30 studies were included. The result of the reporting quality assessment was inferior, items 17 and 19 were not reported at all. The details of five items (items 3, 6, 7, 10, and 18) were not reported. The outcome of the risk of bias assessment suggested that half of the entries (5/10) displayed an unclear risk of bias and a high risk of bias. Blinding with regard to performance bias and detection bias revealed an unclear risk of bias (100%), followed by baseline characteristics (76.67%) and sequence generation (60%). Random outcome assessment showed a high risk of bias (100%). ConclusionThe included animal studies exhibited methodological defects and imprecise reporting. Hence, the ARRIVE guidelines and SYRCLE's RoB tool should be disseminated among basic medical researchers examining CSS for depression to publish studies with low risk of bias and sufficient reporting so that the animal research can promptly be transformed into clinical research.

Representation of Motion Direction in Visual Area MT Accounts for High Sensitivity to Centripetal Motion, Aligning with Efficient Coding of Retinal Motion Statistics.

The overrepresentation of centrifugal motion in the middle temporal visual area (area MT) has long been thought to provide an efficient coding strategy for optic flow processing. However, this overrepresentation compromises the detection of approaching objects, which is essential for survival. In the present study, we revisited this long-held notion by reanalyzing motion selectivity in area MT of three macaque monkeys (two males, one female) using random-dot stimuli instead of spot stimuli. We found no differences in the number of neurons tuned to centrifugal versus centripetal motion; however, centrifugally tuned neurons showed stronger tuning than centripetally tuned neurons. This was attributed to the heightened suppression of responses in centrifugal neurons to centripetal motion compared with that of centripetal neurons to centrifugal motion. Our modeling implies that this intensified suppression accounts for superior detection performance for weak centripetal motion stimuli. Moreover, through Fisher information analysis, we establish that the population sensitivity to motion direction in peripheral vision corresponds well with retinal motion statistics during forward locomotion. While these results challenge established concepts, considering the interplay of logarithmic Gaussian receptive fields and spot stimuli can shed light on the previously documented overrepresentation of centrifugal motion. Significantly, our findings reconcile a previously found discrepancy between MT activity and human behavior, highlighting the proficiency of peripheral MT neurons in encoding motion direction efficiently.SIGNIFICANCE STATEMENT The efficient coding hypothesis states that sensory neurons are tuned to specific, frequently experienced stimuli. Whereas previous work has found that neurons in the middle temporal (MT) area favor centrifugal motion, which results from forward locomotion, we show here that there is no such bias. Moreover, we found that the response of centrifugal neurons for centripetal motion was more suppressed than that of centripetal neurons for centrifugal motion. Combined with modeling, this provides a solution to a previously known discrepancy between reported centrifugal bias in MT and better detection of centripetal motion by human observers. Additionally, we show that population sensitivity in peripheral MT neurons conforms to an efficient code of retinal motion statistics during forward locomotion.

Adaptive bolus calculators for people with type 1 diabetes: A systematic review.

To conduct a systematic review of studies assessing adaptive insulin bolus calculators for people with type 1 diabetes (T1D). Electronic databases (Medline, Embase and Web of Science) were systematically searched from date of inception to 13 October 2022 for single-arm or randomized controlled studies assessing adaptive bolus calculators only, in children or adults with T1D on multiple daily injections or insulin pumps with glycaemic outcomes reported. The Clinicaltrials.gov registry was searched for recently completed studies evaluating decision support in T1D. The quality of extracted studies was assessed using the Standard Quality Assessment criteria and the Cochrane Risk of Bias assessment tool. Six studies were identified. Extracted data were synthesized in a descriptive review because of heterogeneity. All the studies were small feasibility studies or were not suitably powered, and all were deemed to be at a high risk of performance and detection bias because they were unblinded. Overall, these studies did not show a significant glycaemic improvement. Two studies showed a reduction in postprandial time below range or an incremental change in blood glucose concentration; however, these were in controlled environments over a short duration. There are limited clinical trials evaluating adaptive bolus calculators. Although results from small trials or in-silico data are promising, further studies are required to support personalized and adaptive management of T1D.

Handling Bias in Toxic Speech Detection: A Survey

Detecting online toxicity has always been a challenge due to its inherent subjectivity. Factors such as the context, geography, socio-political climate, and background of the producers and consumers of the posts play a crucial role in determining if the content can be flagged as toxic. Adoption of automated toxicity detection models in production can thus lead to a sidelining of the various groups they aim to help in the first place. It has piqued researchers’ interest in examining unintended biases and their mitigation. Due to the nascent and multi-faceted nature of the work, complete literature is chaotic in its terminologies, techniques, and findings. In this article, we put together a systematic study of the limitations and challenges of existing methods for mitigating bias in toxicity detection. We look closely at proposed methods for evaluating and mitigating bias in toxic speech detection. To examine the limitations of existing methods, we also conduct a case study to introduce the concept of bias shift due to knowledge-based bias mitigation. The survey concludes with an overview of the critical challenges, research gaps, and future directions. While reducing toxicity on online platforms continues to be an active area of research, a systematic study of various biases and their mitigation strategies will help the research community produce robust and fair models. 1

Assessing Biases through Visual Contexts

Bias detection in the computer vision field is a necessary task, to achieve fair models. These biases are usually due to undesirable correlations present in the data and learned by the model. Although explainability can be a way to gain insights into model behavior, reviewing explanations is not straightforward. This work proposes a methodology to analyze the model biases without using explainability. By doing so, we reduce the potential noise arising from explainability methods, and we minimize human noise during the analysis of explanations. The proposed methodology combines images of the original distribution with images of potential context biases and analyzes the effect produced in the model’s output. For this work, we first presented and released three new datasets generated by diffusion models. Next, we used the proposed methodology to analyze the context impact on the model’s prediction. Finally, we verified the reliability of the proposed methodology and the consistency of its results. We hope this tool will help practitioners to detect and mitigate potential biases, allowing them to obtain more reliable models.

Donepezil combined with traditional Chinese medicine has promising efficacy on mild cognitive impairment: a systematic review and meta-analysis.

Prior research has shown mixed results regarding the effectiveness of combining donepezil and traditional Chinese medicine (TCM) to treat mild cognitive impairment (MCI). In light of this, our study aims to examine the efficacy and safety of this treatment approach for patients with MCI. We conducted a comprehensive search of various databases, including Medline (via PubMed), Cochrane, Embase, Web of Science, Chinese National Knowledge Infrastructure, Chinese Biomedical Literature Database, Chinese Scientific Journal Database, and Wanfang Database from their inception to November 16, 2022. The selection of studies, risk of bias assessment, and data extraction were carried out independently by two authors. The statistical analysis was performed using STATA. Our meta-analysis included a total of 35 studies with 2,833 patients, published between 2008 and 2022, with intervention durations ranging from 4 weeks to 12 months. However, most of the studies had a high risk of detection bias. Our findings indicated that the combination of donepezil and TCM significantly improved the Montreal Cognitive Assessment (MoCA) score (weighted mean difference [WMD] = 2.79, 95% confidence interval [CI]: 1.82 to 3.75) and the Barthel Index score (WMD = 9.20, 95% CI: 5.39 to 13.00) compared to donepezil alone. However, subgroup analyses showed that the MoCA score did not increase significantly in patients with MCI resulting from cerebrovascular disease (WMD = 1.47, 95% CI: -0.02 to 2.96). The combination of donepezil and TCM may have a more positive effect on cognitive function and activities of daily living in patients with MCI compared to the use of donepezil alone. However, due to the limited quality of the studies included in our analysis, these findings should be interpreted with caution.