Biosynthesized silver nanoparticles prevent bacterial infection in chicken egg model and mitigate biofilm formation on medical catheters.

Investigating the application of innovative antimicrobial surface coatings on medical devices is an important field of research. Many of these coatings have significant drawbacks, including biocompatibility, coating stability and the inability to effectively combat multiple drug-resistant bacteria. In this research, we developed an antibiofilm surface coating for medical catheters using biosynthesized silver nanoparticles (b-Cs-AgNPs) developed using leaves extract of Calliandra surinamensis. Various characterization techniques were employed to thoroughly characterize the synthesized b-Cs-AgNPs and c-AgNPs. b-Cs-AgNPs were compatible with human normal kidney cells and chicken embryos. It did not trigger any skin inflammatory response in in vivo rat model. b-Cs-AgNPs demonstrated potent zone of inhibition of 19.09 mm when subjected to the disc diffusion method in E. coli confirming strong antibacterial property. Different anti-bacterial assays including liquid growth curve, colony counting assay, biofilm formation assay supported the potent antimicrobial efficacy of b-Cs-AgNPs alone and when coated to medical grade catheters. Mechanistic studies reveal the presence of ferulic acid, that was important for the synthesis of b-AgNPs along with enhanced antibacterial effects of b-Cs-AgNPs compared to c-AgNPs, supported by molecular docking analysis. These results together demonstrated the effective role b-Cs-AgNPs in combating infections and mitigating biofilm formations, highlighting their need for further study in the field of biomedical applications.

Generalizing Parkinson's disease detection using keystroke dynamics: a self-supervised approach.

OBJECTIVE: Passive monitoring of touchscreen interactions generates keystroke dynamic signals that can be used to detect and track neurological conditions such as Parkinson's disease (PD) and psychomotor impairment with minimal burden on the user. However, this typically requires datasets with clinically confirmed labels collected in standardized environments, which is challenging, especially for a large subject pool. This study validates the efficacy of a self-supervised learning method in reducing the reliance on labels and evaluates its generalizability. MATERIALS AND METHODS: We propose a new type of self-supervised loss combining Barlow Twins loss, which attempts to create similar feature representations with reduced feature redundancy for samples coming from the same subject, and a Dissimilarity loss, which promotes uncorrelated features for samples generated by different subjects. An encoder is first pre-trained using this loss on unlabeled data from an uncontrolled setting, then fine-tuned with clinically validated data. Our experiments test the model generalizability with controls and subjects with PD on 2 independent datasets. RESULTS: Our approach showed better generalization compared to previous methods, including a feature engineering strategy, a deep learning model pre-trained on Parkinsonian signs, and a traditional supervised model. DISCUSSION: The absence of standardized data acquisition protocols and the limited availability of annotated datasets compromise the generalizability of supervised models. In these contexts, self-supervised models offer the advantage of learning more robust patterns from the data, bypassing the need for ground truth labels. CONCLUSION: This approach has the potential to accelerate the clinical validation of touchscreen typing software for neurodegenerative diseases.

3D printable, injectable amyloid-based composite hydrogel of bovine serum albumin and aloe vera for rapid diabetic wound healing.

Protein-based biomaterials, particularly amyloids, have sparked considerable scientific interest in recent years due to their exceptional mechanical strength, excellent biocompatibility and bioactivity. In this work, we have synthesized a novel amyloid-based composite hydrogel consisting of bovine serum albumin (BSA) and aloe vera (AV) gel to utilize the medicinal properties of the AV gel and circumvent its mechanical frangibility. The synthesized composite hydrogel demonstrated an excellent porous structure, self-fluorescence, non-toxicity, and controlled rheological properties. Moreover, this hydrogel possesses inherent antioxidant and antibacterial properties, which accelerate the rapid healing of wounds. The in vitro wound healing capabilities of the synthesized composite hydrogel were evaluated using 3T3 fibroblast cells. Moreover, the efficacy of the hydrogel in accelerating chronic wound healing via collagen crosslinking was investigated through in vivo experiments using a diabetic mouse skin model. The findings indicate that the composite hydrogel, when applied, promotes wound healing by inducing collagen deposition and upregulating the expression of vascular endothelial growth factor (VEGF) and its receptors. We also demonstrate the feasibility of the 3D printing of the BSA-AV hydrogel, which can be tailored to treat various types of wound. The 3D printed hydrogel exhibits excellent shape fidelity and mechanical properties that can be utilized for personalized treatment and rapid chronic wound healing. Taken together, the BSA-AV hydrogel has great potential as a bio-ink in tissue engineering as a dermal substitute for customizable skin regeneration.

In silico analysis of angiotensin-converting enzyme inhibitory compounds obtained from soybean [Glycine max (L.) Merr.].

Cardiovascular diseases (CVDs) are one of the major reasons for deaths globally. The renin-angiotensin-aldosterone system (RAAS) regulates body hypertension and fluid balance which causes CVD. Angiotensin-converting enzyme I (ACE I) is the central Zn-metallopeptidase component of the RAAS playing a crucial role in maintaining homeostasis of the cardiovascular system. The available drugs to treat CVD have many side effects, and thus, there is a need to explore phytocompounds and peptides to be utilized as alternative therapies. Soybean is a unique legume cum oilseed crop with an enriched source of proteins. Soybean extracts serve as a primary ingredient in many drug formulations against diabetes, obesity, and spinal cord-related disorders. Soy proteins and their products act against ACE I which may provide a new scope for the identification of potential scaffolds that can help in the design of safer and natural cardiovascular therapies. In this study, the molecular basis for selective inhibition of 34 soy phytomolecules (especially of beta-sitosterol, soyasaponin I, soyasaponin II, soyasaponin II methyl ester, dehydrosoyasaponin I, and phytic acid) was evaluated using in silico molecular docking approaches and dynamic simulations. Our results indicate that amongst the compounds, beta-sitosterol exhibited a potential inhibitory action against ACE I.

Indexing Resilience to Heat and Drought Stress in the Wild Relatives of Rapeseed-Mustard.

Wild species are weedy relatives and progenitors of cultivated crops, usually maintained in their centres of origin. They are rich sources of diversity as they possess many agriculturally important traits. In this study, we analysed 25 wild species and 5 U triangle species of Brassica for their potential tolerance against heat and drought stress during germination and in order to examine the early seedling stage. We identified the germplasms based on the mean membership function value (MFV), which was calculated from the tolerance index of shoot length, root length, and biochemical analysis. The study revealed that B. napus (GSC-6) could withstand high temperatures and drought. Other genotypes that were tolerant to the impact of heat stress were B. tournefortii (RBT 2002), D. gomez-campoi, B. tournefortii (Rawa), L. sativum, and B. carinata (PC-6). C. sativa resisted drought but did not perform well when subjected to high temperatures. Tolerance to drought was observed in B. fruticulosa (Spain), B. tournefortii (RBT 2003), C. bursa-pastoris (late), D. muralis, C. abyssinica (EC694145), C. abyssinica (EC400058) and B. juncea (Pusa Jaikisan). This investigation contributes to germplasm characterization and the identification of the potential source of abiotic stress tolerance in the Brassica breeding programme. These identified genotypes can be potential sources for transferring the gene(s)/genomic regions that determine tolerance to the elite cultivars.

Keystroke-Dynamics for Parkinson's Disease Signs Detection in an At-Home Uncontrolled Population: A New Benchmark and Method.

Parkinson's disease (PD) is the second most prevalent neurodegenerative disease disorder in the world. A prompt diagnosis would enable clinical trials for disease-modifying neuroprotective therapies. Recent research efforts have unveiled imaging and blood markers that have the potential to be used to identify PD patients promptly, however, the idiopathic nature of PD makes these tests very hard to scale to the general population. To this end, we need an easily deployable tool that would enable screening for PD signs in the general population. In this work, we propose a new set of features based on keystroke dynamics, i.e., the time required to press and release keyboard keys during typing, and used to detect PD in an ecologically valid data acquisition setup at the subject's homes, without requiring any pre-defined task. We compare and contrast existing models presented in the literature and present a new model that combines a new type of keystroke dynamics signal representation using hold time and flight time series as a function of key types and asymmetry in the time series using a convolutional neural network. We show how this model achieves an Area Under the Receiving Operating Characteristic curve ranging from 0.80 to 0.83 on a dataset of subjects who actively interacted with their computers for at least 5 months and positively compares against other state-of-the-art approaches previously tested on keystroke dynamics data acquired with mechanical keyboards.

Angiotensin-converting enzyme inhibitory peptides and isoflavonoids from soybean [Glycine max (L.) Merr.].

Angiotensin-converting enzyme I (ACE I) is a zinc-containing metallopeptidase involved in the renin-angiotensin system (RAAS) that helps in the regulation of hypertension and maintains fluid balance otherwise, which results in cardiovascular diseases (CVDs). One of the leading reasons of global deaths is due to CVDs. RAAS also plays a central role in maintaining homeostasis of the CV system. The commercial drugs available to treat CVDs possess several fatal side effects. Hence, phytochemicals like peptides having plant-based origin should be explored and utilized as alternative therapies. Soybean is an important leguminous crop that simultaneously possesses medicinal properties. Soybean extracts are used in many drug formulations for treating diabetes and other disorders and ailments. Soy proteins and its edible products such as tofu have shown potential inhibitory activity against ACE. Thus, this review briefly describes various soy proteins and products that can be used to inhibit ACE thereby providing new scope for the identification of potential candidates that can help in the design of safer and natural treatments for CVDs.

A novel framework to estimate cognitive impairment via finger interaction with digital devices.

Measuring cognitive function is essential for characterizing brain health and tracking cognitive decline in Alzheimer's Disease and other neurodegenerative conditions. Current tools to accurately evaluate cognitive impairment typically rely on a battery of questionnaires administered during clinical visits which is essential for the acquisition of repeated measurements in longitudinal studies. Previous studies have shown that the remote data collection of passively monitored daily interaction with personal digital devices can measure motor signs in the early stages of synucleinopathies, as well as facilitate longitudinal patient assessment in the real-world scenario with high patient compliance. This was achieved by the automatic discovery of patterns in the time series of keystroke dynamics, i.e. the time required to press and release keys, by machine learning algorithms. In this work, our hypothesis is that the typing patterns generated from user-device interaction may reflect relevant features of the effects of cognitive impairment caused by neurodegeneration. We use machine learning algorithms to estimate cognitive performance through the analysis of keystroke dynamic patterns that were extracted from mechanical and touchscreen keyboard use in a dataset of cognitively normal (n = 39, 51% male) and cognitively impaired subjects (n = 38, 60% male). These algorithms are trained and evaluated using a novel framework that integrates items from multiple neuropsychological and clinical scales into cognitive subdomains to generate a more holistic representation of multifaceted clinical signs. In our results, we see that these models based on typing input achieve moderate correlations with verbal memory, non-verbal memory and executive function subdomains [Spearman's ρ between 0.54 (P < 0.001) and 0.42 (P < 0.001)] and a weak correlation with language/verbal skills [Spearman's ρ 0.30 (P < 0.05)]. In addition, we observe a moderate correlation between our typing-based approach and the Total Montreal Cognitive Assessment score [Spearman's ρ 0.48 (P < 0.001)]. Finally, we show that these machine learning models can perform better by using our subdomain framework that integrates the information from multiple neuropsychological scales as opposed to using the individual items that make up these scales. Our results support our hypothesis that typing patterns are able to reflect the effects of neurodegeneration in mild cognitive impairment and Alzheimer's disease and that this new subdomain framework both helps the development of machine learning models and improves their interpretability.

Quantitative 3-Dimensional Photographic Assessment of Breast Cosmesis After Whole Breast Irradiation for Early Stage Breast Cancer: A Secondary Analysis of a Randomized Clinical Trial.

PURPOSE: Our purpose was to use 3-dimensional (3D) surface photography to quantitatively measure breast cosmesis within the framework of a randomized clinical trial of conventionally fractionated (CF) and hypofractionated (HF) whole breast irradiation (WBI); to identify how 3D measurements are associated with patient- and physician-reported cosmesis; and to determine whether objective measures of breast symmetry varied by WBI treatment arm or transforming growth factor ß 1 (TGFß1) status. METHODS AND MATERIALS: From 2011 to 2014, 287 women age ≥40 with ductal carcinoma in situ or early-stage invasive breast cancer were enrolled in a multicenter trial and randomized to HF-WBI or CF-WBI with a boost. Three-dimensional surface photography was performed at 3 years posttreatment. Patient-reported cosmetic outcomes were recorded with the Breast Cancer Treatment Outcome Scale. Physician-reported cosmetic outcomes were assessed by the Radiation Therapy Oncology Group scale. Volume ratios and 6 quantitative measures of breast symmetry, termed F1-6C, were calculated using the breast contour and fiducial points assessed on 3D surface images. Associations between all metrics, patient- and physician-reported cosmesis, treatment arm, and TGFß1 genotype were performed using the Kruskal-Wallis test and multivariable logistic regression models. RESULTS: Among 77 (39 CF-WBI and 38 HF-WBI) evaluable patients, both patient- and physician-reported cosmetic outcomes were significantly associated with the F1C vertical symmetry measure (both P < .05). Higher dichotomized F1C and volumetric symmetry measures were associated with improved patient- and physician-reported cosmesis on multivariable logistic regression (both P ≤ .05). There were no statistically significant differences in vertical symmetry or volume measures between treatment arms. Increased F6C horizontal symmetry was observed in the CF-WBI arm (P = .05). Patients with the TGFß1 C-509T variant allele had lower F2C vertical symmetry measures (P = .02). CONCLUSIONS: Quantitative 3D image-derived measures revealed comparable cosmetic outcomes with HF-WBI compared with CF-WBI. Our findings suggest that 3D surface imaging may be a more sensitive method for measuring subtle cosmetic changes than global patient- or physician-reported assessments.

Structure-Based Immunogenicity Prediction of Uricase from Fungal (Aspergillus flavus), Bacterial (Bacillus subtillis) and Mammalian Sources Using Immunoinformatic Approach.

Gout is a common rheumatic condition caused due to increase in serum uric acid level (hyperuricemia). Uricase is for lowering the level of uric acid but unfortunately, it is not produced in humans due to evolutionary changes. Therefore, it is administered to humans from outside in case of the high uric acid level in blood. A different formulation of uricase from bacterial, fungal, and mammalian sources is present in the market for the treatment of hyperuricemia conditions. Uricase formulation showed immunogenic response due to the occurrence of hypersensitivity reaction during the treatment that results in poor patient compliance. The purpose of this study was to clarify the variation of Uricase immunogenicity from different sources. We have used some immunoinformatic approaches to analyze and understand some structural aspects of immunogenic and allergenic epitopes of Uricase by calculation of relative frequency for eleven global alleles. As per our knowledge, this is the first immunoinformatic study of Uricase (structural based immunogenicity prediction) that deciphered the high immunogenic nature of Uricase but no significant difference in immunogenicity was found among Uricase isolated from Aspergillus flavus, Bacillus subtillis, and mammalian source. This study gives a further lead to develop some methods (include bioengineering of less immunogenic version of the uricase or utilizing the homologous enzymes) for minimizing immune response or search new sources of uricase that could be less or non-immunogenic.