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Background: Type 2 diabetes mellitus (T2DM) poses a significant health-care challenge and is a primary contributor to both mortality and morbidity. Diabetic autonomic neuropathy (DAN) is a frequently occurring complication associated with diabetes mellitus. Autonomic nerve damage leading to cardiac autonomic dysfunction and peripheral sympathetic neuropathy can lead to changes in heart rate variability (HRV) and galvanic skin response (GSR), respectively. Aims and Objectives: The study was designed and done to evaluate the impact of T2DM on HRV and GSR for assessing DAN. Materials and Methods: A total of 100 individuals diagnosed with T2DM (cases) and 100 healthy individuals (controls) participated in this study. Physio Pac (PC-2004) was used to record the necessary data. Statistical analysis was performed using the non-parametric Mann--Whitney test to see the comparison of the cases and controls. In addition, to measure the statistical relationship between the duration of the disease and the HRV and GSR parameters, Spearman's rank correlation coefficient was utilized. The study received approval from the Ethical and Research Committee at the Government Medical College, Patiala. Results: Significant statistical differences were observed between the cases and controls in all time domain measures, as well as GSR and the majority of frequency domain measures of HRV. The correlation analysis revealed a strong association between the duration of T2DM and a decrease in HRV as well as an increase in GSR. Conclusion: The findings from the present study provide evidence that T2DM has an impact on HRV, which suggests the presence of cardiac autonomic neuropathy among diabetic patients, leading to an increased risk of cardiovascular morbidity. In addition, the study indicates that reduced sudomotor function, as assessed by galvanic skin response (GSR), serves as a predictive measure for autonomic neuropathy. Therefore, these tests can serve as valuable tools in facilitating the assessment of DAN. [ABSTRACT FROM AUTHOR]

Clinical procedures, which require a large number of personnel and medical resources, receive the majority of the current focus on thyroid nodule diagnosis. An automated thyroid ultrasound nodule identification system is built using image texture data and convolutional neural networks in this study. The following are the major phases: The underlying stages in building a ultrasound thyroid knob dataset incorporate gathering positive and negative examples, normalizing pictures, and portioning the knob region. Second, a texture features model is built by selecting features, reducing the dimensionality of the data, and extracting texture features. Third, deep neural networks in move learning are utilized to create an element model of the knob in an image. The convolutional brain network highlight model and the surface component model were combined to create the brand-new knob include model known as the Feature Fusion Network. The Feature Fusion Network is used to prepare and improve performance over a single organization in order to create a demonstrative model for deep neural networks that can adapt to a variety of knob features. 1874 clinical ultrasonography thyroid knobs were gathered for this investigation. The musical normal F-score considering Accuracy and Review is utilized as an assessment metric. With an F-score of 92.52 percent, the study’s findings suggest that the Element Combination Organization can differentiate between benign and harmful thyroid knobs. As far as execution, this methodology performs better compared to standard ML procedures and convolutional neural networks.

Mobile wallets have gained widespread popularity as a convenient, secure, and user-friendly payment method embraced by consumers. However, the pace of mobile wallet adoption has exhibited variations across different markets, primarily due to a range of factors. In this study, we present an all-encompassing examination of existing literature, aiming to pinpoint the fundamental elements influencing the sustainable acceptance of mobile wallets by consumers. Through an exhaustive analysis of 80 research papers published between 2010 and 2022, we discern the prevailing factors that hold sway over the adoption of mobile wallets. Our scrutinization highlights that factor such as perceived sustainability, usefulness, ease of use, security, social influence, trustworthiness, and compatibility stand out as the most formidable propellants of mobile wallet adoption. Furthermore, our investigation uncovers hurdles that hinder the wider acceptance of mobile wallets, encompassing insufficient awareness, perceived intricacies, and lingering uncertainties regarding the technology’s sustainability. Our in-depth evaluation underscores the necessity of comprehending consumer perspectives and dispositions towards mobile wallets to galvanize their adoption sustainably. The culmination of our inquiry involves a discourse on the implications drawn from our discoveries, catering to researchers and practitioners vested in fostering the sustainable adoption of mobile wallets.

Heat pipe is used to extract heat generated from a surface and applicable to cooling of the many components. Some of the main applications of heat pipes are space crafts, computer systems, permafrost cooling, heat exchangers and thermal storage sub systems. In this work, an experiment will be carried out with an indirect heat composite pipe located with different orientation, heat input and mass flow. Thermal resistance, heat transfer coefficient and thermal efficiency are determined, which are applicable for laptop cooling and solar heating. Heat input 25 to 150 watts, with orientation angles 0, 30, 45, 90 degrees, mass flow rate are 0.01kg/sec, 0.02kg/sec and 0.03kg/sec are considered.

Background Non-small-cell lung cancer (NSCLC) patients often demonstrate varying clinical courses and outcomes, even within the same tumor stage. This study explores deep learning applications in medical imaging allowing for the automated quantification of radiographic characteristics and potentially improving patient stratification. Methods and findings We performed an integrative analysis on 7 independent datasets across 5 institutions totaling 1,194 NSCLC patients (age median = 68.3 years [range 32.5-93.3], survival median = 1.7 years [range 0.0-11.7]). Using external validation in computed tomography (CT) data, we identified prognostic signatures using a 3D convolutional neural network (CNN) for patients treated with radiotherapy (n = 771, age median = 68.0 years [range 32.5-93.3], survival median = 1.3 years [range 0.0-11.7]). We then employed a transfer learning approach to achieve the same for surgery patients (n = 391, age median = 69.1 years [range 37.2-88.0], survival median = 3.1 years [range 0.0-8.8]). We found that the CNN predictions were significantly associated with 2-year overall survival from the start of respective treatment for radiotherapy (area under the receiver operating characteristic curve [AUC] = 0.70 [95% CI 0.63-0.78], p < 0.001) and surgery (AUC = 0.71 [95% CI 0.60-0.82], p < 0.001) patients. The CNN was also able to significantly stratify patients into low and high mortality risk groups in both the radiotherapy (p < 0.001) and surgery (p = 0.03) datasets. Additionally, the CNN was found to significantly outperform random forest models built on clinical parameters-including age, sex, and tumor node metastasis stage-as well as demonstrate high robustness against test-retest (intraclass correlation coefficient = 0.91) and inter-reader (Spearman's rank-order correlation = 0.88) variations. To gain a better understanding of the characteristics captured by the CNN, we identified regions with the most contribution towards predictions and highlighted the importance of tumor-surrounding tissue in patient stratification. We also present preliminary findings on the biological basis of the captured phenotypes as being linked to cell cycle and transcriptional processes. Limitations include the retrospective nature of this study as well as the opaque black box nature of deep learning networks. Conclusions Our results provide evidence that deep learning networks may be used for mortality risk stratification based on standard-of-care CT images from NSCLC patients. This evidence motivates future research into better deciphering the clinical and biological basis of deep learning networks as well as validation in prospective data., Author(s): Ahmed Hosny 1, Chintan Parmar 1, Thibaud P. Coroller 1, Patrick Grossmann 1, Roman Zeleznik 1, Avnish Kumar 1, Johan Bussink 2, Robert J. Gillies 3, Raymond H. Mak [...]

Abstract Background Free radical stress leads to tissue injury and can eventually to arthritis, atherosclerosis, diabetes mellitus, neurodegenerative diseases and carcinogenesis. Several studies are ongoing worldwide to find natural antioxidants of plant origin. We assessed the in-vitro antioxidant activities and screened the phytochemical constituents of methanolic extracts of Pyrostegia venusta (Ker Gawl) Miers. Methods We evaluated the antioxidant potential and phytochemical constituents of P. venusta using 1,1-Diphenyl-2-picrylhydrazyl (DPPH), 2, 2'-azinobis-3-ethylbenzothiazoline-6-sulfonic acid (ABTS) and ferric reducing antioxidant power (FRAP) assays. Gas chromatography-mass spectroscopy (GC-MS) studies were also undertaken to assess the phytochemical composition of the flower extracts. Results Phytochemical analyses revealed the presence of terpenoids, alkaloids, tannins, steroids, and saponins. The reducing ability of both extracts was in the range (in μm Fe(II)/g) of 112.49-3046.98 compared with butylated hydroxytoluene (BHT; 63.56 ± 2.62), catechin (972.02 ± 0.72 μm) and quercetin 3208.27 ± 31.29. A significant inhibitory effect of extracts of flowers (IC50 = 0.018 ± 0.69 mg/ml) and roots (IC50 = 0.026 ± 0.94 mg/ml) on ABTS free radicals was detected. The antioxidant activity of the extracts of flowers (95%) and roots (94%) on DPPH radicals was comparable with that of ascorbic acid (98.9%) and BHT (97.6%). GC-MS study revealed the presence of myoinositol, hexadecanoic acid, linoleic acid, palmitic acid and oleic acid in the flower extracts. Conclusion These data suggest that P. venusta is a natural source of antioxidants. The extracts of flowers and roots of P. venusta contain significant amounts of phytochemicals with antioxidative properties and could serve as inhibitors or scavengers of free radicals. P. venusta could be exploited as a potential source for plant-based pharmaceutical products. These results could form a sound basis for further investigation in the potential discovery of new natural bioactive compounds.

Photo-electrochemical properties of fluorine-doped tin oxide (FTO)/nickel oxide-nickel (Ni0.98Si0.02O2-Ni) with isolated Si sites are discussed. The Ni0.98Si0.02O2-Ni photo-electrocatalyst without any noble metal was prepared using a one-step chemical reduction precipitation method. Photo-electrochemical properties were investigated by cyclic voltammetry and chronoamperometry techniques in aqueous alkaline solution at pH 8.0. The photo-electrochemical response of the catalyst showed sensitivity to visible and UV light. The catalyst produced hydrogen from water at a rate of 0.5 mmol g−1 h−1 with the reduction of CO2 into CO under visible light in the absence of any light sensitizer or a noble metal. The metallic Ni amount in the catalyst system was optimized to obtain the best photo-electrocatalyst. The first principle DFT study showed that the incorporation of Si sites allowed absorbance of visible light. The catalyst was characterized by powder x-ray diffraction (PXRD), scanning electron microscope (SEM), transmission electron microscope (TEM), X-ray photoelectron spectroscopy (XPS), inductively coupled plasma- atomic emission spectroscopy (ICP-AES), Brunauer–Emmett–Teller (BET), cyclic voltammetry, and chronoamperometry. The products were analyzed by gas chromatography- thermal conductivity detector (GC-TCD). [ABSTRACT FROM AUTHOR]

Background: Chronic exposure to flour dust has been implicated to the reduction of lung function. Pulmonary function tests are an important measure for the diagnosis and management of respiratory disorders. Workers in bakery, confectionary and flour mills are exposed to high amount of flour dust. Objectives: To identify the changes in spirometric parameters in bakery, confectionary and flour mill workers of Patiala, Punjab (India). Methods: The study was conducted in 200 non smoker males working in bakery, confectionary and flour mills and compared with 200 non smoker, non exposed males (Controls) from the same locality. Results: Spirometric parameters were significantly reduced over a period of 10 year exposure to flour dust in males working in various food industries. The changes were evident over a 10 year exposure. The decline in lung function in subjects exposed for more than 10 years when compared to a 10 year exposure period was not significant. Conclusion: Inhalation of industrial dust during work can lead to temporary or permanent changes in ventilator capacity. The decrease in pulmonary function is linked to years of exposure initially. [ABSTRACT FROM AUTHOR]

Lactobacillus casei and Lactobacillus acidophilus have been evaluated for the inhibitory activity of pro-inflammatory cytokine gene expressions. Collagen induced arthritic model was used. L. casei and L. acidophilus of 2 × 108 CFU/mL was orally administered for 28 days. IL-6, IL-1β, TNF, IL-4 and IL-10 in serum were estimated using ELISA. mRNA gene expressions of the same cytokines in knee joints were also analyzed using RT PCR. Paw thickness was measured at the end of each week. Lactobacillus treatment statistically increased IL-4 and IL-10 levels while decreased IL-6, IL-1β and TNF. mRNA expression of IL-6, IL-1β and TNF gene were highly stimulated in control group animals joint samples. Lactobacillus and drug treatment were restored to normal in other groups. mRNA expression of IL-10 and IL-4 were highly expressed in Lactobacillus administered groups. Lactobacillus was effective in alleviating the inflammatory responses and thus arthritis symptoms. Lactobacillus could be considered as a dietary agent against arthritis. [ABSTRACT FROM AUTHOR]

Background: Heart Rate Variability (HRV), which is a measure of the cardiac autonomic tone, displays physiological changes throughout the menstrual cycle. The functions of the ANS in various phases of the menstrual cycle were examined in some studies. Aims and Objectives: The aim of our study was to observe the effect of menstrual cycle on cardiac autonomic function parameters in healthy females. Materials and Methods: A cross-sectional (observational) study was conducted on 50 healthy females, in the age group of 18-25 years. Heart Rate Variability (HRV) was recorded by Physio Pac (PC-2004). The data consisted of Time Domain Analysis and Frequency Domain Analysis in menstrual, proliferative and secretory phase of menstrual cycle. Data collected was analysed statistically using student's pair t-test. Results: The difference in mean heart rate, LF power%, LFnu and HFnu in menstrual and proliferative phase was found to be statistically significant. The difference in mean RR, Mean HR, RMSSD (the square root of the mean of the squares of the successive differences between adjacent NNs.), NN50 (the number of pairs of successive NNs that differ by more than 50 ms), pNN50 (the proportion of NN50 divided by total number of NNs.), VLF (very low frequency) power, LF (low frequency) power, LF power%, HF power %, LF/HF ratio, LFnu and HFnu was found to be statistically significant in proliferative and secretory phase. The difference in Mean RR, Mean HR, LFnu and HFnu was found to be statistically significant in secretory and menstrual phases. Conclusion: From the study it can be concluded that sympathetic nervous activity in secretory phase is greater than in the proliferative phase, whereas parasympathetic nervous activity is predominant in proliferative phase. [ABSTRACT FROM AUTHOR]