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Adherence to antihypertensives is crucial for control of blood pressure. This study analyzed factors and interventions that could affect adherence to antihypertensives in the US. PubMed, Scopus, Web of Science, and Embase were searched on January 21, 2022 and December 25, 2023 for studies on the adherence to antihypertensives in the US. Nineteen studies and 23 545 747 patients were included in the analysis, which showed that adherence to antihypertensives was the highest among Whites (OR: 1.47, 95% CI 1.34–1.61 compared to African Americans). Employment status and sex were associated with insignificant differences in adherence rates. In contrast, marital status yielded a significant difference where unmarried patients demonstrated low adherence rates compared to married ones (OR: 0.8, 95% CI 0.67–0.95). On analysis of comorbidities, diabetic patients reported lower adherence to antihypertensives (OR: 0.95, 95% CI 0.92–0.97); furthermore, patients who did not have Alzheimer showed higher adherence rates. Different BMIs did not significantly affect the adherence rates. Patients without insurance reported significantly lower adherence rates than insured patients (OR: 3.93, 95% CI 3.43–4.51). Polypill users had higher adherence rates compared with the free‐dose combination (OR: 1.21, 95% CI 1.2–1.21), while telepharmacy did not prove to be as effective. Lower adherence rates were seen among African Americans, uninsured, or younger patients. Accordingly, interventions such as fixed‐dose combinations should be targeted at susceptible groups. Obesity and overweight did not affect the adherence to antihypertensives.

Background and AimLow serum Vitamin D levels have been associated with diabetic nephropathy (DN). Our study aimed to analyse the serum levels of vitamin D in patients suffering from DN and the subsequent changes in serum vitamin D levels as the disease progresses.MethodsPubMed, Embase, SCOPUS and Web of Science were searched using keywords such as ‘25 hydroxyvitamin D’ and ‘diabetic nephropathy’. We included observational studies that reported the association between the serum 25 hydroxy vitamin D levels and diabetic nephropathy without restriction to age, gender, and location. R Version 4.1.2 was used to perform the meta-analysis. The continuous outcomes were represented as mean difference (MD) and standard deviation (SD) and dichotomous outcomes as risk ratios (RR) with their 95% confidence interval (CI).ResultsTwenty-three studies were included in our analysis with 7722 patients. Our analysis revealed that vitamin D was significantly lower in diabetic patients with nephropathy than those without nephropathy (MD: −4.32, 95% CI: 7.91–0.74, p-value = .0228). On comparing diabetic patients suffering from normoalbuminuria, microalbuminuria, or macroalbuminuria, we found a significant difference in serum vitamin D levels across different groups. Normoalbuminuria versus microalbuminuria showed a MD of −1.69 (95% CI: −2.28 to −1.10, p-value = .0002), while microalbuminuria versus macroalbuminuria showed a MD of (3.75, 95% CI: 1.43–6.06, p-value = .0058), proving that serum vitamin D levels keep declining as the disease progresses. Notwithstanding, we detected an insignificant association between Grade 4 and Grade 5 DN (MD: 2.29, 95% CI: −2.69–7.28, p-value = .1862).ConclusionSerum Vitamin D levels are lower among DN patients and keep declining as the disease progresses, suggesting its potential benefit as a prognostic marker. However, on reaching the macroalbuminuria stage (Grades 4 and 5), vitamin D is no longer a discriminating factor.

Non-adherence is a barrier to the control of hypertension. This study aimed to analyze the factors and interventions affecting adherence rates in the US. PubMed, Scopus, Web of Science, and Embase were searched on January 21 st, 2022 for studies on the adherence to anti-hypertensives in the US. R software (4.2.1) and RevMan (5.4) were used for the analysis. Our analysis showed that non-adherence to antihypertensives was higher in Hispanics compared with Blacks (OR= 1.25, p-value= 0.0335) with further analysis showing lower non-adherence rates among Whites compared with Blacks (OR= 0.63, p-value= 0.0049). Younger individuals had higher non-adherence rates (53%) compared with individuals older than 60 (46%), however, no statistically significant difference was detected between both groups. Unemployment was associated with decreased adherence (OR= 0.40, p-value< 0.00001), while gender and marital status yielded insignificant associations. Patients who suffered from multiple comorbidities (OR= 0.43, p-value= 0.004), depression (OR= 0.65, p-value <0.00001), and poor mental health (OR= 0.38, p-value< 0.00001) had lower adherence rates. Heavy alcohol consumption was associated with a decline in adherence, conversely, obesity didn't significantly affect the adherence rates. Regarding interventions, Fixed-dose combination had lower non-adherence rates compared with the control group (OR= 0.83, p-value= 0.0029), while telepharmacy didn't prove to be as effective. Lastly, exercise was associated with higher adherence (OR= 3.08, p-value< 0.00001). Race and unemployment play a role in adherence to anti-hypertensives. Patients who suffer from depression, multiple comorbidities, and heavy alcohol intake had lower adherence rates. Accordingly, interventions such as fixed-dose combinations and exercise should be targeted at susceptible groups.

Studies have shown an increased incidence of metabolic syndrome (MS) among irritable bowel syndrome (IBS) patients; we aimed to assess the eligibility of IBS as a risk factor for MS. PubMed, Scopus, Embase, and Web of Science were searched on the 1st of January 2023. Only observational controlled studies were included. Analysis was conducted by RevMan software version 5.4. IBS was associated with an increased incidence of MS (RR = 2.05, 95% CI = 1.50 to 2.79, p-value >0.00001). A significant association was seen between IBS, abdominal obesity (RR = 1.28, p-value = 0.0003), and increased waist circumference (MD = 5.01, 95% CI = 1.29 to 8.72, p-value = 0.008). IBS patients didn't have an increased risk of diabetes (RR= 1.29, 95% CI = 0.85 to 1.98, p-value = 0.23), however, they had increased HOMA- IR (MD = 0.21, 95% CI = 0.15 to 0.26, p-value < 0.00001). Analysis of blood pressure revealed an association between systolic not diastolic hypertension and IBS (MD = -0.50, 95% CI = -0.60 to -0.40, p-value >0.00001). Higher levels of LDL cholesterol (MD = 5.98, 95% CI = 0.91 to 11.05, p-value = 0.02), total cholesterol (MD = 12.21, 95% CI = 6.23 to 18.18, p-value >0.0001), and triglyceride (MD = 11.93, 95% CI = 11.55 to 12.31, p-value >0.00001) were detected among IBS patients. IBS patients are at increased risk for MS and its components. Accordingly, patients should be screened for MS, and preventive programs should be implemented.

The purpose of this study is to determine the influence of Covid-19 on the intention to purchase online fashion clothing. A structured questionnaire was used to collect data from 231 customers in an online survey. The researchers utilised exploratory factor analysis to uncover the most important elements that influence customer behaviour while purchasing online fashion items. Multiple regression study revealed that situational variables, practical motive, and safety and precaution had a positive and substantial influence on customers' online buy intention for fashion items. Hedonic incentive, on the other hand, had a minor impact, despite being beneficial on purpose. A structured questionnaire was shared with the respondents for data collection. The Questionnaire was floated between individuals who purchase fashion apparel online. The questions assessing the four predictors were first subjected to an exploratory factor analysis. Second, multiple regression was used to determine the influence of these variables on online purchase intentions among customers.

Unlike bilayers or a few layers thick materials, heterostructures are designer materials formed by assembling different monolayers in any desired sequence. As a result, while multilayer materials come with their intrinsic properties, heterostructures can be tailor-made to suit specific applications. Taking SiH-CdCl 2 as a representative system, we show the potential of heterostructures for several applications, like piezoelectricity, photocatalytic water splitting, and tunnel field effect transistor (TFET). Our study confirms that the characteristics of the heterostructure mainly depend on the potential difference between the constituent monolayers. From the vast database of available layered materials, many such combinations with a suitable potential difference are expected to have similar properties. Our work points to a vast pool of assembled materials with multifunctionality, an excellent asset for next-generation device applications.

The task of identifying out-of-domain (OOD) input examples directly at test-time has seen renewed interest recently due to increased real world deployment of models. In this work, we focus on OOD detection for natural language sentence inputs to task-based dialog systems. Our findings are three-fold: First, we curate and release ROSTD (Real Out-of-Domain Sentences From Task-oriented Dialog) - a dataset of 4K OOD examples for the publicly available dataset from (Schuster et al. 2019). In contrast to existing settings which synthesize OOD examples by holding out a subset of classes, our examples were authored by annotators with apriori instructions to be out-of-domain with respect to the sentences in an existing dataset. Second, we explore likelihood ratio based approaches as an alternative to currently prevalent paradigms. Specifically, we reformulate and apply these approaches to natural language inputs. We find that they match or outperform the latter on all datasets, with larger improvements on non-artificial OOD benchmarks such as our dataset. Our ablations validate that specifically using likelihood ratios rather than plain likelihood is necessary to discriminate well between OOD and in-domain data. Third, we propose learning a generative classifier and computing a marginal likelihood (ratio) for OOD detection. This allows us to use a principled likelihood while at the same time exploiting training-time labels. We find that this approach outperforms both simple likelihood (ratio) based and other prior approaches. We are hitherto the first to investigate the use of generative classifiers for OOD detection at test-time.

An effective recipe for building seq2seq, non-autoregressive, task-oriented parsers to map utterances to semantic frames proceeds in three steps: encoding an utterance \\(x\\), predicting a frame's length |y|, and decoding a |y|-sized frame with utterance and ontology tokens. Though empirically strong, these models are typically bottlenecked by length prediction, as even small inaccuracies change the syntactic and semantic characteristics of resulting frames. In our work, we propose span pointer networks, non-autoregressive parsers which shift the decoding task from text generation to span prediction; that is, when imputing utterance spans into frame slots, our model produces endpoints (e.g., [i, j]) as opposed to text (e.g., \"6pm\"). This natural quantization of the output space reduces the variability of gold frames, therefore improving length prediction and, ultimately, exact match. Furthermore, length prediction is now responsible for frame syntax and the decoder is responsible for frame semantics, resulting in a coarse-to-fine model. We evaluate our approach on several task-oriented semantic parsing datasets. Notably, we bridge the quality gap between non-autogressive and autoregressive parsers, achieving 87 EM on TOPv2 (Chen et al. 2020). Furthermore, due to our more consistent gold frames, we show strong improvements in model generalization in both cross-domain and cross-lingual transfer in low-resource settings. Finally, due to our diminished output vocabulary, we observe 70% reduction in latency and 83% reduction in memory at beam size 5 compared to prior non-autoregressive parsers.

Measuring automatic speech recognition (ASR) system quality is critical for creating user-satisfying voice-driven applications. Word Error Rate (WER) has been traditionally used to evaluate ASR system quality; however, it sometimes correlates poorly with user perception/judgement of transcription quality. This is because WER weighs every word equally and does not consider semantic correctness which has a higher impact on user perception. In this work, we propose evaluating ASR output hypotheses quality with SemDist that can measure semantic correctness by using the distance between the semantic vectors of the reference and hypothesis extracted from a pre-trained language model. Our experimental results of 71K and 36K user annotated ASR output quality show that SemDist achieves higher correlation with user perception than WER. We also show that SemDist has higher correlation with downstream Natural Language Understanding (NLU) tasks than WER.

Word Error Rate (WER) has been the predominant metric used to evaluate the performance of automatic speech recognition (ASR) systems. However, WER is sometimes not a good indicator for downstream Natural Language Understanding (NLU) tasks, such as intent recognition, slot filling, and semantic parsing in task-oriented dialog systems. This is because WER takes into consideration only literal correctness instead of semantic correctness, the latter of which is typically more important for these downstream tasks. In this study, we propose a novel Semantic Distance (SemDist) measure as an alternative evaluation metric for ASR systems to address this issue. We define SemDist as the distance between a reference and hypothesis pair in a sentence-level embedding space. To represent the reference and hypothesis as a sentence embedding, we exploit RoBERTa, a state-of-the-art pre-trained deep contextualized language model based on the transformer architecture. We demonstrate the effectiveness of our proposed metric on various downstream tasks, including intent recognition, semantic parsing, and named entity recognition.