Outcomes of Physicians' Communication Goals During Patient Interactions.

During healthcare visits, physicians may set communication goals such as providing their patient with information about treatment; however, no recommendations exist regarding which goals physicians should prioritize during their often-brief interactions with patients. Two studies examined five communication goals (providing information, reducing distress, increasing patient satisfaction, increasing patient adherence, and encouraging hope) in the context of physician-patient interactions and their relationship with patient and physician outcomes. In Study 1, audio-recordings of physician-patient interactions were coded by research assistants for goal-related content. In Study 2, patients reported their physician's use of each goal during the interaction. In both studies, patients and physicians reported visit outcomes. Within-study meta-analyses suggested that the goal of reducing distress, but not the other goals, was consistently related to improved outcomes in Study 1. All goals were related to improved outcomes in Study 2. We then computed sample-size-weighted meta-analytic effects of each goal on each outcome across both studies. These results suggested that all of the goals had similar-sized positive relationships with patient and physician outcomes across studies. These findings suggest that physicians should generally approach consultations with communication goals in mind, but prioritizing efforts to reduce distress may be particularly beneficial

Spectral Analysis of Mid-IR Excesses of White Dwarfs

In our Spitzer 24 \mu m survey of hot white dwarfs (WDs) and archival Spitzer study of pre-WDs, i.e., central stars of planetary nebulae (CSPNs), we found mid-IR excesses for -15 WDs/pre-WDs. These mid-IR excesses are indicative of the presence of circumstellar dust that could be produced by sub-planetary objects. To further assess the nature of these IR-excesses, we have obtained Spitzer IRS, Gemini NIRI and Michelle, and KPNO 4m echelle spectra of these objects. In this paper we present the analysis of these spectroscopic observations and discuss the nature of these IR excesses.Comment: Part of PlanetsbeyondMS/2010 proceedings http://arxiv.org/html/1011.660

Automatic age estimation based on facial aging patterns

While recognition of most facial variations, such as identity, expression, and gender, has been extensively studied, automatic age estimation has rarely been explored. In contrast to other facial variations, aging variation presents several unique characteristics which make age estimation a challenging task. This paper proposes an automatic age estimation method named AGES (AGing pattErn Subspace). The basic idea is to model the aging pattern, which is defined as the sequence of a particular individual\u27s face images sorted in time order, by constructing a representative subspace. The proper aging pattern for a previously unseen face image is determined by the projection in the subspace that can reconstruct the face image with minimum reconstruction error, while the position of the face image in that aging pattern will then indicate its age. In the experiments, AGES and its variants are compared with the limited existing age estimation methods (WAS and AAS) and some well-established classification methods (kNN, BP, C4.5, and SVM). Moreover, a comparison with human perception ability on age is conducted. It is interesting to note that the performance of AGES is not only significantly better than that of all the other algorithms, but also comparable to that of the human observers.<br /

Image preprocessing in classification and identification of diabetic eye diseases

Diabetic eye disease (DED) is a cluster of eye problem that affects diabetic patients. Identifying DED is a crucial activity in retinal fundus images because early diagnosis and treatment can eventually minimize the risk of visual impairment. The retinal fundus image plays a significant role in early DED classification and identification. An accurate diagnostic model’s development using a retinal fundus image depends highly on image quality and quantity. This paper presents a methodical study on the significance of image processing for DED classification. The proposed automated classification framework for DED was achieved in several steps: image quality enhancement, image segmentation (region of interest), image augmentation (geometric transformation), and classification. The optimal results were obtained using traditional image processing methods with a new build convolution neural network (CNN) architecture. The new built CNN combined with the traditional image processing approach presented the best performance with accuracy for DED classification problems. The results of the experiments conducted showed adequate accuracy, specificity, and sensitivity. © 2021, The Author(s)

Phosphorus-sulfur heterocycles incorporating an O-P(S)-O or O-P(S)-S-S-P(S)-O scaffold : one-pot synthesis and crystal structure study

The authors are grateful to the University of St Andrews for financial support.A new one-pot preparative route was developed to synthesize novel organophosphorus sulfur heterocycles via the reaction of the four-membered ring thionation reagent [2,4-diferrocenyl 1,3,2,4-diathiadiphosphetane 2,4-disulfide (FcLR, a ferrocene analogue of Lawesson’s reagent)] and alkenyl/aryl-diols and I2 (or SOCl2) in the presence of triethylamine. Therefore, a series of five- to ten-membered heterocycles bearing an O-P(S)-O or an O-P(S)-S-S-P(S)-O linkage were synthesized. The synthesis features a novel application of the multicomponent reaction, providing an efficient and environmentally benign method for the preparation of the unusual phosphorus-sulfur heterocycles. Seven representative X-ray structures confirm the formation of these heterocycles.Publisher PDFPeer reviewe

Textural feature based intelligent approach for neurological abnormality detection from brain signal data

The diagnosis of neurological diseases is one of the biggest challenges in modern medicine, which is a major issue at the moment. Electroencephalography (EEG) recordings is usually used to identify various neurological diseases. EEG produces a large volume of multi-channel time-series data that neurologists visually analyze to identify and understand abnormalities within the brain and how they propagate. This is a time-consuming, error-prone, subjective, and exhausting process. Moreover, recent advances in EEG classification have mostly focused on classifying patients of a specific disease from healthy subjects using EEG data, which is not cost effective as it requires multiple systems for checking a subject’s EEG data for different neurological disorders. This forces researchers to advance their work and create a single, unified classification framework for identifying various neurological diseases from EEG signal data. Hence, this study aims to meet this requirement by developing a machine learning (ML) based data mining technique for categorizing multiple abnormalities from EEG data. Textural feature extractors and ML-based classifiers are used on time-frequency spectrogram images to develop the classification system. Initially, noises and artifacts are removed from the signal using filtering techniques and then normalized to reduce computational complexity. Afterwards, normalized signals are segmented into small time segments and spectrogram images are generated from those segments using short-time Fourier transform. Then two histogram based textural feature extractors are used to calculate features separately and principal component analysis is used to select significant features from the extracted features. Finally, four different ML based classifiers are used to categorize those selected features into different disease classes. The developed method is tested on four real-time EEG datasets. The obtained result has shown potential in classifying various abnormality types, indicating that it can be utilized to identify various neurological abnormalities from brain signal data

Convolutional neural network for multi-class classification of diabetic eye disease

Prompt examination increases the chances of effective treatment of Diabetic Eye Disease (DED) and reduces the likelihood of permanent deterioration of vision. A key tool commonly used for the initial diagnosis of patients with DED or other eye disorders is the screening of retinal fundus images. Manual detection with these images is, however, labour intensive and time consuming. As deep learning (DL) has recently been demonstrated to provide impressive benefits to clinical practice, researchers have attempted to use DL method to detect retinal eye diseases from retinal fundus photographs. DL techniques in machine learning (ML) have achieved state-of-the-art performance in the binary classification of healthy and diseased retinal fundus images while the classification of multi-class retinal eye diseases remains an open challenge. Multiclass DED is therefore considered in this study seeking to develop an automated classification framework for DED. Detecting multiple DEDs from retinal fundus images is an important research topic with practical consequences. Our proposed model was tested on various retinal fundus images gathered from the publicly available dataset and annotated by an ophthalmologist. This experiment was conducted employing a new convolutional neural network (CNN) model. Our proposed model for multi-class classification achieved a maximum accuracy of 81.33%, sensitivity of 100%, and specificity of 100%

Contribution of the gp120 V3 loop to envelope glycoprotein trimer stability in primate immunodeficiency viruses

The V3 loop of the human immunodeficiency virus type 1 (HIV-1) gp120 exterior envelope glycoprotein (Env) becomes exposed after CD4 binding and contacts the coreceptor to mediate viral entry. Prior to CD4 engagement, a hydrophobic patch located at the tip of the V3 loop stabilizes the non-covalent association of gp120 with the Env trimer of HIV-1 subtype B strains. Here, we show that this conserved hydrophobic patch (amino acid residues 307, 309 and 317) contributes to gp120-trimer association in HIV-1 subtype C, HIV-2 and SIV. Changes that reduced the hydrophobicity of these V3 residues resulted in increased gp120 shedding and decreased Envmediated cell-cell fusion and virus entry in the different primate immunodeficiency viruses tested. Thus, the hydrophobic patch is an evolutionarily conserved element in the tip of the gp120 V3 loop that plays an essential role in maintaining the stability of the pre-triggered Env trimer in diverse primate immunodeficiency viruses