Association between falls in elderly women and chronic diseases and drug use: cross sectional study.

OBJECTIVE: To assess the associations between having had a fall and chronic diseases and drug use in elderly women. DESIGN: Cross sectional survey, using data from the British women's heart and health study. SETTING: General practices in 23 towns in Great Britain. PARTICIPANTS: 4050 women aged 60-79 years. MAIN OUTCOME MEASURE: Whether women had had falls in the previous 12 months. RESULTS: The prevalence of falling increased with increasing numbers of simultaneously occurring chronic diseases. However, no such relation with falling was found in the fully adjusted data for the number of drugs used. Circulatory disease, chronic obstructive pulmonary disease, depression, and arthritis were all associated with an increased odds of falling. The fully adjusted, population attributable risk of falling associated with having at least one chronic disease was 32.2% (95% confidence interval 19.6% to 42.8%). Only two classes of drugs (hypnotics and anxiolytics, and antidepressants) were independently associated with an increased odds of falling. Each class was associated with an increase of about 50% in the odds of falling, and each had a population attributable risk of < 5%. CONCLUSION: Chronic diseases and multiple pathology are more important predictors of falling than polypharmacy

Parolees' physical closeness to health service providers: a study of California parolees.

We studied a sample of parolees and health service providers in the state of California in 2005-2006 to examine the relative physical closeness to health providers (and the potential demand of these providers) of parolees based on their demographic and prior offending characteristics. Although African-American and Latino parolees have more health providers nearby, these providers have considerably more potential demand. The health providers near long-term prisoners and sex offenders have more potential demand. The results suggest inequity in access to services, as minority parolees and those with greater needs may live near more impacted providers. The results also suggest some differences in access based on rural, suburban, or urban location

A sustainable approach for synthesis of zinc oxide nanoparticle by Aloe barbadensis and its application in photocatalytic decolouration of commercial dyes.

Zinc oxide nanoparticles (ZnONPs) biosynthesis is gaining popularity since it is environmentally safe and can withstand a wide range of environmental conditions. The widely known medicinal herb Aloe barbadensis was employed to create ZnONPs in this work. XRD (X-Ray Diffraction), EDAX (Energy dispersive X-ray microanalysis), and TEM (Transmission Electron Microscopy) were also used to characterise the produced ZnONPs. In XRD, the produced ZnONPs revealed crystalline character, with an average size of 30 50 nm. TEM was used to determine spherical morphology. Under ultraviolet irradiation, the photocatalytic decolorization of Sudan IV, Crystal Violet (CV), and Acridine Orange (AO) by biogenic produced ZnONPs was studied. Using all three dyes (10-50 ppm) throughout a 4-hour incubation time, the produced ZnONPs showed 100% photocatalytic decolorization activity&nbsp; &nbsp; &nbsp

Importance of negative sampling in weak label learning

Weak-label learning is a challenging task that requires learning from data "bags" containing positive and negative instances, but only the bag labels are known. The pool of negative instances is usually larger than positive instances, thus making selecting the most informative negative instance critical for performance. Such a selection strategy for negative instances from each bag is an open problem that has not been well studied for weak-label learning. In this paper, we study several sampling strategies that can measure the usefulness of negative instances for weak-label learning and select them accordingly. We test our method on CIFAR-10 and AudioSet datasets and show that it improves the weak-label classification performance and reduces the computational cost compared to random sampling methods. Our work reveals that negative instances are not all equally irrelevant, and selecting them wisely can benefit weak-label learning

Effect of meals with varying glycemic index on blood glucose response in type 2 diabetes mellitus

Background: In diabetes mellitus (DM) glucose is underused, producing hyperglycaemia. Dietary interventions would be effective in management of glycemic control in DM. The glycemic index (GI) and glycemic load (GL) takes into account the glycemic response. Foods with contrasting GI when incorporated into a meal are able to differentially modify glycemia. Currently there is no universal approach to the optimal dietary strategy for DM. Also, little is known about whether this is dependent on the size and composition of the meal. The purpose of the study was to evaluate the blood glucose response to mixed meals (with varying GI and GL) served to Type 2 DM subjects and to determine the relationship between GI, GL and Postprandial Plasma glucose levels (PPG) in Type 2 DM.Methods: This study included 30 Type 2 DM subjects and 30 Non Diabetic Subjects. The subjects were served Hospital based and Home based diet. The FPG (Fasting Plasma Glucose) and PPG values were analysed for comparing the effect of both the diets on plasma glucose levels.Results: After analysis of study data we found that plasma glucose response (FPG-126±6.1 mg/dl, PPG-144.3±4.5 mg/dl) for hospital based low GI meals is significantly lower (p <0.0001) than after one week follow up home based mixed GI meals, (FPG-135±4.5 mg/dl, PPG 158.3±4.5 mg/dl).Conclusions: It was concluded in the study that Plasma Glucose shows a positive response to high GI foods and this may aggravate the hyperglycemia already present in Type 2 DM. Low GI diets may be helpful in reducing risks related to Type 2 DM.

Ondansetron exposure during pregnancy is not associated with risk of congenital malformations: evidence from a meta-analysis

Ondansetron is widely used drug for treatment of morning sickness and hyperemesis gravidarum. However, whether exposure to ondansetron during pregnancy is associated with risk of congenital malformations or not remains debatable. The present meta-analysis was performed for published cohort/registry-based studies which evaluated the association between ondansetron exposure and risk of congenital malformations. Major congenital malformations were considered as the primary outcome measure. Specific abnormalities like cardiac malformation, septal defect, cleft lip/palate, hypospadias, and genitourinary abnormalities were considered as secondary outcome measures along with spontaneous abortion/miscarriage, stillbirth, preterm delivery, and low birth weight babies. Pooled analysis was done using the Mantle-Hanzle method, random effect model and were expressed as odds ratio (OR) with 95% CI. Fourteen studies were included in systematic review. There was no significant difference for major congenital malformations [n=12; OR: 1.12 (95% CI: 0.93-1.36), I2=96], septal defect [n=5; OR: 1.39 (95% CI: 1.01-1.91), I2=48%], cleft lip/palate [n=3; OR: 1.11 (95% CI: 0.80-1.53), I2=41%] between ondansetron exposed and control arms. However, a greater number of events were found in control arm than intervention arm for cardiac defect [n=7; OR: 1.26 (95% CI: 1.09-1.45), I2=71%; p=0.002]. We also observed a greater number of events for stillbirth and pre-term labour in control arm than in intervention arm with OR: 1.57 (95% CI: 1.24-1.97); p=0.0001 and OR: 1.33 (95% CI: 1.05-1.69); p=0.02, respectively. This meta-analysis concludes that ondansetron exposure during pregnancy is not associated with any increased risk of major congenital malformations, septal /cardiac defect, cleft lip/palate, spontaneous abortion/miscarriage, stillbirth, pre-term labour and low birth weight babies

Visual Tracking: An Experimental Survey

There is a large variety of trackers, which have been proposed in the literature during the last two decades with some mixed success. Object tracking in realistic scenarios is difficult problem, therefore it remains a most active area of research in Computer Vision. A good tracker should perform well in a large number of videos involving illumination changes, occlusion, clutter, camera motion, low contrast, specularities and at least six more aspects. However, the performance of proposed trackers have been evaluated typically on less than ten videos, or on the special purpose datasets. In this paper, we aim to evaluate trackers systematically and experimentally on 315 video fragments covering above aspects. We selected a set of nineteen trackers to include a wide variety of algorithms often cited in literature, supplemented with trackers appearing in 2010 and 2011 for which the code was publicly available. We demonstrate that trackers can be evaluated objectively by survival curves, Kaplan Meier statistics, and Grubs testing. We find that in the evaluation practice the F-score is as effective as the object tracking accuracy (OTA) score. The analysis under a large variety of circumstances provides objective insight into the strengths and weaknesses of trackers

Imprecise Label Learning: A Unified Framework for Learning with Various Imprecise Label Configurations

Learning with reduced labeling standards, such as noisy label, partial label, and multiple label candidates, which we generically refer to as \textit{imprecise} labels, is a commonplace challenge in machine learning tasks. Previous methods tend to propose specific designs for every emerging imprecise label configuration, which is usually unsustainable when multiple configurations of imprecision coexist. In this paper, we introduce imprecise label learning (ILL), a framework for the unification of learning with various imprecise label configurations. ILL leverages expectation-maximization (EM) for modeling the imprecise label information, treating the precise labels as latent variables.Instead of approximating the correct labels for training, it considers the entire distribution of all possible labeling entailed by the imprecise information. We demonstrate that ILL can seamlessly adapt to partial label learning, semi-supervised learning, noisy label learning, and, more importantly, a mixture of these settings. Notably, ILL surpasses the existing specified techniques for handling imprecise labels, marking the first unified framework with robust and effective performance across various challenging settings. We hope our work will inspire further research on this topic, unleashing the full potential of ILL in wider scenarios where precise labels are expensive and complicated to obtain.Comment: 29 pages, 3 figures, 16 tables, preprin

LoFT: Local Proxy Fine-tuning For Improving Transferability Of Adversarial Attacks Against Large Language Model

It has been shown that Large Language Model (LLM) alignments can be circumvented by appending specially crafted attack suffixes with harmful queries to elicit harmful responses. To conduct attacks against private target models whose characterization is unknown, public models can be used as proxies to fashion the attack, with successful attacks being transferred from public proxies to private target models. The success rate of attack depends on how closely the proxy model approximates the private model. We hypothesize that for attacks to be transferrable, it is sufficient if the proxy can approximate the target model in the neighborhood of the harmful query. Therefore, in this paper, we propose \emph{Local Fine-Tuning (LoFT)}, \textit{i.e.}, fine-tuning proxy models on similar queries that lie in the lexico-semantic neighborhood of harmful queries to decrease the divergence between the proxy and target models. First, we demonstrate three approaches to prompt private target models to obtain similar queries given harmful queries. Next, we obtain data for local fine-tuning by eliciting responses from target models for the generated similar queries. Then, we optimize attack suffixes to generate attack prompts and evaluate the impact of our local fine-tuning on the attack's success rate. Experiments show that local fine-tuning of proxy models improves attack transferability and increases attack success rate by $39\%$, $7\%$, and $0.5\%$ (absolute) on target models ChatGPT, GPT-4, and Claude respectively