A randomized double-blind, placebo-controlled trial to evaluate the safety and efficacy of live Bifidobacterium longum CECT 7347 (ES1) and heat-treated Bifidobacterium longum CECT 7347 (HT-ES1) in participants with diarrhea-predominant irritable bowel syndrome.

To determine the efficacy of the probiotic Bifidobacterium longum CECT 7347 (ES1) and postbiotic heat-treated Bifidobacterium longum CECT 7347 (HT-ES1) in improving symptom severity in adults with diarrhea-predominant irritable bowel syndrome (IBS-D), a randomised, double-blind, placebo-controlled trial with 200 participants split into three groups was carried out. Two capsules of either ES1, HT-ES1 or placebo were administered orally, once daily, for 84 days (12 weeks). The primary outcome was change in total IBS-Symptom Severity Scale (IBS-SSS) score from baseline, compared to placebo. Secondary outcome measures were stool consistency, quality of life, abdominal pain severity and anxiety scores. Safety parameters and adverse events were also monitored. The change in IBS-SSS scores from baseline compared to placebo, reached significance in the ES1 and HT-ES1 group, on Days 28, 56 and 84. The decrease in mean IBS-SSS score from baseline to Day 84 was: ES1 (-173.70 [±75.60]) vs placebo (-60.44 [±65.5]) (p < .0001) and HT-ES1 (-177.60 [±79.32]) vs placebo (-60.44 [±65.5]) (p < .0001). Secondary outcomes included changes in IBS-QoL, APS-NRS, stool consistency and STAI-S and STAI-T scores, with changes from baseline to Day 84 being significant in ES1 and HT-ES1 groups, compared to the placebo group. Both ES1 and HT-ES1 were effective in reducing IBS-D symptom severity, as evaluated by measures such as IBS-SSS, IBS-QoL, APS-NRS, stool consistency, and STAI, in comparison to the placebo. These results are both statistically significant and clinically meaningful, representing, to the best of the authors' knowledge, the first positive results observed for either a probiotic or postbiotic from the same strain, in this particular population.

What is already known on this topicIBS is a chronic functional gastrointestinal disorder characterized by abdominal pain, bloating and abnormalities in stool frequency or form. The gut microbiota of people living with IBS differs markedly to the microbiota of healthy individuals. Gut microbiota may play a key role in IBS aetiology and IBS symptoms may be alleviated by modulating the gut microbiota. Several proposed ways to modulate gut health include normalizing the gut microbiota, preventing the overgrowth of pathogenic bacteria, modulating visceral afferent pathways, and enhancing intestinal barrier function. However, significant heterogeneity between studies, study quality and population, study design and concerns about sample size have limited national and supranational bodies from recommending probiotics for IBS. Further well-powered, randomized, repeatable and controlled trials are warranted.What this study addsThe results of this study substantially contribute to the IBS research field, firstly by providing clinically meaningful and statistically significant results from a rigorous, well designed randomized, placebo-controlled trial and secondly, by exploring the use of postbiotics in IBS, an area of research still in its infancy. Probiotic (ES1) and postbiotic (HT-ES1) supplementation significantly reduced IBS symptom severity scores compared to placebo. This study met primary and secondary outcomes and strongly suggest that ES1 and HT-ES1 could be beneficial in the management of IBS.How this study might affect research, practice, or policyThis study adds to the current evidence base, supporting the use of probiotic/postbiotics for IBS. This research could be used to inform health professionals about using probiotics in IBS and help improve the quality of life and wellbeing for people living with the condition.

An analysis on the approximate controllability of neutral impulsive stochastic integrodifferential inclusions via resolvent operators.

This article focuses on the approximate controllability of impulsive neutral stochastic integrodifferential inclusions in Hilbert spaces. We used resolvent operators, fixed point approaches, and semigroup theory to achieve the article's main results. First, we focus on the existence of approximate controllability, and we develop the existence results with nonlocal conditions. At last, an application is provided to illustrate the concept.

Impact of core polarity on smectic B-induced hydrogen bond liquid crystals.

The novel series of hydrogen bond liquid crystals were synthesized from the 2-methylglutaric acid (MGA) and 4-alkyloxybenzoic acid (nOBA) compounds. The induced smectic B phase with different texture (spine texture, needle texture, mosaic texture, natural mosaic texture and marble texture) were identified by polarizing optical microscope. Due to breaking of in-plane rotational symmetry within molecular layers, smectic B phase is tempted by suppressing other usual mesophases. The mesomorphic transition temperature, enthalpy and entropy values were calculated by differential scanning calorimeter which strongly proves the existence of mesomorphism. H-bond interaction and functional groups were confirmed by the observed peak between 2910 and 2954 cm-1 in the FTIR spectra. Thermal stability and extended mesophase width (for MGA + 12OBA = 31.1) of Sm B mesophase were reported and it clearly reveals the existence of mono-phase variance in the MGA + nOBA HBLC complex. Due to the steric effect, and the increased molecular core polarity, the highly stabilized Sm B phase with different textures were observed while varying alkyloxy carbon number n = 7 to 12. Further, the origination of Sm B phase and its detailed characteristics were reported.

Evaluation of xanthene-appended quinoline hybrids as potential leads against antimalarial drug targets.

A series of fused heterocycle xanthene-appended quinoline 6a-n was successfully synthesized with regioselectivity and characterized using IR, 1H NMR, 13C NMR, and mass spectral data. Molecular docking was performed to find the binding efficacy of all these newly synthesized compounds towards thirteen antimalarial drug targets. Molecular dynamics simulation was carried out to predict the stability of the ligand-bound complex in a solvent medium. Blind and site-directed docking with compounds 6a-n against 13 drug targets revealed most of the ligands to have a good binding affinity with the targets. Analysis on the basis of binding energy, binding modalities of the ligands, intermolecular interactions, and pharmacophore, we identified only one of the ligand-receptor complexes to provide better results. Molecular dynamic simulation of the selected receptor-ligand complex revealed that the synthesized compound had a better binding affinity with the receptor than the native ligand complex. Further analysis of the synthesized ligand in the laboratory may prove promising results in the search for potential antimalarial drugs.

Deep Learning Techniques for the Effective Prediction of Alzheimer's Disease: A Comprehensive Review.

"Alzheimer's disease" (AD) is a neurodegenerative disorder in which the memory shrinks and neurons die. "Dementia" is described as a gradual decline in mental, psychological, and interpersonal qualities that hinders a person's ability to function autonomously. AD is the most common degenerative brain disease. Among the first signs of AD are missing recent incidents or conversations. "Deep learning" (DL) is a type of "machine learning" (ML) that allows computers to learn by doing, much like people do. DL techniques can attain cutting-edge precision, beating individuals in certain cases. A large quantity of tagged information with multi-layered "neural network" architectures is used to perform analysis. Because significant advancements in computed tomography have resulted in sizable heterogeneous brain signals, the use of DL for the timely identification as well as automatic classification of AD has piqued attention lately. With these considerations in mind, this paper provides an in-depth examination of the various DL approaches and their implementations for the identification and diagnosis of AD. Diverse research challenges are also explored, as well as current methods in the field.

Rosette-induced separation of T cells by acoustophoresis.

Breakthrough cell therapies for the treatment of cancers require the separation of specific cells, such as T cells, from the patient's blood. Current cell therapy processes rely on magnetic separation, which adds clinical risk and requires elevated manufacturing controls due to the added foreign material that constitutes the magnetic beads. Acoustophoresis, a method that uses ultrasound for cell separation, has demonstrated label-free enrichment of T cells from blood, but residual other lymphocytes limit the ultimate purity of the output T cell product. Here, to increase the specificity of acoustophoresis, we use affinity reagents to conjugate red blood cells with undesired white blood cells, resulting in a cell-cell complex (rosette) of increased acoustic mobility. We achieve up to 99% purity of T cells from blood products, comparable to current standards of magnetic separation, yet without the addition of separation particles.

Developing priorities for quality improvement in acute medicine using a modified Delphi method A consensus process hosted by the Society for Acute Medicine Quality Improvement Committee (SAM-QI).

INTRODUCTION: The SAM Quality Improvement Committee (SAM-QI), set up in 2016, has worked over the last year to determine the priority Acute Medicine QI topics. They have also discussed and put forward proposals to improve QI training for Acute Medicine professionals. METHODS: A modified Delphi process was completed over four rounds to determine priority QI topics. Online meetings were also used to develop proposals for QI training. RESULTS: Same Day Emergency Care (SDEC) was chosen as the priority topic for QI work within Acute Medicine. CONCLUSION: The SAM-QI group settled on SDEC being the priority topic for Acute Medicine QI development. Throughout the Delphi process SAM-QI has also developed proposals for QI training that will help Acute Medicine professionals deliver coordinated meaningful improvements in care.

Hybrid Model for Detection of Cervical Cancer Using Causal Analysis and Machine Learning Techniques.

Cervical cancer has become the third most common form of cancer in the in-universe, after the widespread breast cancer. Human papillomavirus risk of infection is linked to the majority of cancer cases. Preventive care, the most expensive way of fighting cancer, can protect about 37% of cancer cases. The Pap smear examination is a standard screening procedure for the initial screening of cervical cancer. However, this manual test procedure generates many false-positive outcomes due to individual errors. Various researchers have extensively investigated machine learning (ML) methods for classifying cervical Pap cells to enhance manual testing. The random forest method is the most popular method for anticipating features from a high-dimensional cancer image dataset. However, the random forest method can get too slow and inefficient for real-time forecasts when too many decision trees are used. This research proposed an efficient feature selection and prediction model for cervical cancer datasets using Boruta analysis and SVM method to deal with this challenge. A Boruta analysis method is used. It is improved from of random forest method and mainly discovers feature subsets from the data source that are significant to assigned classification activity. The proposed model's primary aim is to determine the importance of cervical cancer screening factors for classifying high-risk patients depending on the findings. This research work analyses cervical cancer and various risk factors to help detect cervical cancer. The proposed model Boruta with SVM and various popular ML models are implemented using Python and various performance measuring parameters, i.e., accuracy, precision, F1-Score, and recall. However, the proposed Boruta analysis with SVM performs outstanding over existing methods.

MRI Brain Tumor Image Classification Using a Combined Feature and Image-Based Classifier.

Brain tumor classification plays a niche role in medical prognosis and effective treatment process. We have proposed a combined feature and image-based classifier (CFIC) for brain tumor image classification in this study. Carious deep neural network and deep convolutional neural networks (DCNN)-based architectures are proposed for image classification, namely, actual image feature-based classifier (AIFC), segmented image feature-based classifier (SIFC), actual and segmented image feature-based classifier (ASIFC), actual image-based classifier (AIC), segmented image-based classifier (SIC), actual and segmented image-based classifier (ASIC), and finally, CFIC. The Kaggle Brain Tumor Detection 2020 dataset has been used to train and test the proposed classifiers. Among the various classifiers proposed, the CFIC performs better than all other proposed methods. The proposed CFIC method gives significantly better results in terms of sensitivity, specificity, and accuracy with 98.86, 97.14, and 98.97%, respectively, compared with the existing classification methods.

Cervical Cancer Diagnostics Healthcare System Using Hybrid Object Detection Adversarial Networks.

Cervical cancer is one of the common cancers among women and it causes significant mortality in many developing countries. Diagnosis of cervical lesions is done using pap smear test or visual inspection using acetic acid (staining). Digital colposcopy, an inexpensive methodology, provides painless and efficient screening results. Therefore, automating cervical cancer screening using colposcopy images will be highly useful in saving many lives. Nowadays, many automation techniques using computer vision and machine learning in cervical screening gained attention, paving the way for diagnosing cervical cancer. However, most of the methods rely entirely on the annotation of cervical spotting and segmentation. This paper aims to introduce the Faster Small-Object Detection Neural Networks (FSOD-GAN) to address the cervical screening and diagnosis of cervical cancer and the type of cancer using digital colposcopy images. The proposed approach automatically detects the cervical spot using Faster Region-Based Convolutional Neural Network (FR-CNN) and performs the hierarchical multiclass classification of three types of cervical cancer lesions. Experimentation was done with colposcopy data collected from available open sources consisting of 1,993 patients with three cervical categories, and the proposed approach shows 99% accuracy in diagnosing the stages of cervical cancer.

Partial velocity slip effect on working magneto non-Newtonian nanofluids flow in solar collectors subject to change viscosity and thermal conductivity with temperature.

Solar thermal collectors distribute, capture, and transform the solar energy into a solar thermal concentration device. The present paper provides a mathematical model for analyzing the flow characteristics and transport of heat to solar collectors (SCs) from non-Newtonian nanofluids. The non-Newtonian power-law scheme is considered for the nanofluid through partial slip constraints at the boundary of a porous flat surface. The nanofluid is assumed to differ in viscosity and thermal conductivity linearly with temperature changes and the magnetic field is appliqued to the stream in the transverse direction. The method of similarity conversion is used to convert the governing structure of partial differential formulas into the system of ordinary differential ones. Using the Keller box procedure, the outcoming ordinary differential formulas along with partial slip constraints are numerically resolved. A discussion on the flowing and heat transport characteristics of nanofluid influenced by power law index, Joule heating parameter, MHD parameter and slip parameters are included from a physical point of view. Comparison of temperature profiles showed a marked temperature increase in the boundary layer due to Joule heating. The thickness of the motion boundary-layer is minimized and the transport of heat through boundary-layer is improved with the partial slip velocity and magnetic parameters rising. Finally, With an increase in the Eckert number, the distribution of temperature within boundary layer is increased.

A numerical frame work of magnetically driven Powell-Eyring nanofluid using single phase model.

The current investigation aims to examine heat transfer as well as entropy generation analysis of Powell-Eyring nanofluid moving over a linearly expandable non-uniform medium. The nanofluid is investigated in terms of heat transport properties subjected to a convectively heated slippery surface. The effect of a magnetic field, porous medium, radiative flux, nanoparticle shapes, viscous dissipative flow, heat source, and Joule heating are also included in this analysis. The modeled equations regarding flow phenomenon are presented in the form of partial-differential equations (PDEs). Keller-box technique is utilized to detect the numerical solutions of modeled equations transformed into ordinary-differential equations (ODEs) via suitable similarity conversions. Two different nanofluids, Copper-methanol (Cu-MeOH) as well as Graphene oxide-methanol (GO-MeOH) have been taken for our study. Substantial results in terms of sundry variables against heat, frictional force, Nusselt number, and entropy production are elaborate graphically. This work's noteworthy conclusion is that the thermal conductivity in Powell-Eyring phenomena steadily increases in contrast to classical liquid. The system's entropy escalates in the case of volume fraction of nanoparticles, material parameters, and thermal radiation. The shape factor is more significant and it has a very clear effect on entropy rate in the case of GO-MeOH nanofluid than Cu-MeOH nanofluid.

Eco-friendly synthesis of silver nanoparticles from the whole plant of Cleome viscosa and evaluation of their characterization, antibacterial, antioxidant and antidiabetic properties.

The current research is to develop an easy and eco-friendly method for the synthesis of three different concentrations of silver nanoparticles (1mMCvAgNPs, 2mMCvAgNPs and 3mMCvAgNPs) using aqueous whole plant extract of Cleome viscosa and to evaluate their antibacterial, antioxidant and antidiabetic properties. CvAgNPs were characterized by Using UV-vis spectrophotometer, X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FT-IR), scanning electron microscope (SEM) and transmission electron microscope (TEM). The formation of CvAgNPs was confirmed by the observation of band between 250 nm to 600 nm UV-vis spectrum. The crystalline structure of CvAgNPs with a face-centered cubic (FCC) was confirmed by XRD. The responsible phytochemicals for the reduction and capping material of CvAgNPs were observed with FT-IR. The SEM analysis confirmed the size and shapes of CvAgNPs. The CvAgNPs have shown the rich content of total phenolic and total flavonoid components. The CvAgNPs have shown significant antibacterial activity on multi drug resistance Gram-negative and Gram-positive bacteria and also have shown significant strong antioxidant activities (DPPH, ABTS, H2O2 scavenging, Phosphomolybdenum assay and reducing power). The inhibitory action of CvAgNPs on α-glucosidase and α-amylase was stronger than the inhibitory action of acarbose. To best of our knowledge, this is the first attempt on the synthesis of AgNPs using C. viscosa whole plant aqueous extract. The synthesized CvAgNPs exhibited good antimicrobial, antioxidant and antidiabetic properties. Hence, to validate our results, the in vivo studies at the molecular level are needed to develop Cleome viscosa as an antibacterial, antioxidant and anti-diabetic agent.

Rationally designed imidazole derivative as colorimetric and fluorometric sensor for selective, qualitative and quantitative cyanide ion detection in real time samples.

A new imidazole derivative of 1,2-diaminoanthraquinone and fluorene-2-carboxaldehyde was designed as a sensor B2 to selectively detect the cyanide (CN-) ion through colorimetric and/or fluorometric methods. The photochemical characterizations of sensor B2 were tested using absorption and emission spectral studies in CH3CN-H2O (8:2) semi-aqueous medium. An excited state proton transfer process (ESIPT) was proved by theoretical and spectral studies. The colorimetric and fluorescence detection limit of CN- ion was found to be 5.3 × 10-6 M and 4.11 × 10-8 M, respectively. 1H NMR titration, electrochemical and DFT studies were supported the removal of -NH proton from B2. In order to utilize this sensor in real-time applications, we developed a test cassette which is coated with sensor B2 detected the presence of CN- ion in the food sample with endogenous cyanide ion.

Challenges faced in diabetes risk prediction among an indigenous South Asian population in India using the Indian Diabetes Risk Score.

OBJECTIVES: Indigenous populations around the world have a higher health disparity and an increased risk of diabetes. Scientific literature on the prevalence of diabetes in India is not available, and the current work is a pilot study to explore the risk of diabetes in one such indigenous population in India. STUDY DESIGN: This is a cross-sectional survey and screening study. METHODS: The study took place in a remote tribal hamlet of Machuru in South India. A door-to-door survey was conducted in the hamlet with a population of 555. The Indian Diabetes Risk Score (IDRS) questionnaire was completed by 160 individuals older than 25 years. Capillary blood glucose levels were measured to compare the glycaemic status with the predicted IDRS. RESULTS: Of 160 adults who completed the questionnaire, 37 were at high risk (23.13%) as per the IDRS, 52 at medium risk (32.5%) and 71 at low risk (44.38%). None of the respondents knew their family history of diabetes owing to the lack of awareness about the condition. Interestingly, the villagers had a sedentary lifestyle owing to their unique family dynamics but a healthy diet. Five participants were diagnosed with diabetes, and 18 were diagnosed with impaired fasting glucose or prediabetes. CONCLUSIONS: The IDRS might not be an accurate measure to understand the risk of diabetes in this particular population owing to their unique family dynamics and a lack of awareness about diabetes. The best possible way to assess the diabetes risk might be through blood examination.

Mannich Base Based (ß-Amino Carbonyl Compound) Receptor for Efficient and Selective Sensing of Fluoride Ions.

Mannich Base (MB) based (ß-Amino carbonyl compound) (4) was synthesized by adopting one pot Mannich reaction by taking 3,4-dihydroxybenzaldehyde, aniline and acetophenone as starting materials and the product was characterized using analytical techniques such as FT-IR, NMR, mass spectrometry, UV-Visible spectroscopy. The compound 4 utilized as colorimetric sensor for selective sensing of F- ions. The particular sensing of F- is obviously noticeable to the naked eye with a dissimilar colour variation. The F- detection mechanism has been examined by UV-visible and 1H NMR titration experimentations. The 1H NMR titration data describes the deprotonation of OH and N-H protons by F- as a noticeable stage in the detection. Furthermore, the studies was also carried out for reproduction of deprotonated 4 using trifluoroacetic acid as protonating agent. In addition, Boolean logic gates were fabricated for demonstrating the fluoride sensing mechanism and compound 4 in methyl cellulose biofilm was also been made and effectively used for film based detection of fluoride ion.

A study on medical Internet of Things and Big Data in personalized healthcare system.

Personalized healthcare systems deliver e-health services to fulfill the medical and assistive needs of the aging population. Internet of Things (IoT) is a significant advancement in the Big Data era, which supports many real-time engineering applications through enhanced services. Analytics over data streams from IoT has become a source of user data for the healthcare systems to discover new information, predict early detection, and makes decision over the critical situation for the improvement of the quality of life. In this paper, we have made a detailed study on the recent emerging technologies in the personalized healthcare systems with the focus towards cloud computing, fog computing, Big Data analytics, IoT and mobile based applications. We have analyzed the challenges in designing a better healthcare system to make early detection and diagnosis of diseases and discussed the possible solutions while providing e-health services in secure manner. This paper poses a light on the rapidly growing needs of the better healthcare systems in real-time and provides possible future work guidelines.

Synthesis, DNA binding and in-vitro cytotoxicity studies on novel bis-pyrazoles.

A new series of bis-pyrazoles 6a-t were synthesized from 3,5-dimethyl pyrazole using sequential approach. All these compounds were characterized by IR, 1H NMR, 13C NMR and mass spectral data. The interaction of newly synthesized bis-pyrazoles with DNA was investigated through molecular docking and absorption spectroscopic technique. Among all bis-pyrazoles compounds, the 6h compound showed lower conformational energy through in silico analysis. The interaction of each molecule in this series 6a-t with the various concentrations of DNA was examined through the UV-visible spectroscopic studies. The UV-visible spectroscopy studies on the specific binding of compound 6a, 6b, 6g, 6h, 6d, 6i, 6k, 6n, 6s with DNA have exhibited spectral shifts and the results were discussed. In further the compounds 6a-t were subjected to the in-vitro cytotoxicity studies against human pancreatic adenocarcinoma, human non-small cell lung carcinoma cell lines. Among the screened compounds, N-(3-isopropoxy-1-isopropyl-4-(3,5-dimethyl-1H-pyrazol-1-yl)-1H-pyrazol-5-yl)cyclobutane carboxamide and N-(5'-Isopropoxy-2'-isopropyl-3,5-dimethyl-2'H-[1,4'] bipyrazolyl-3'-yl)-dimethane sulfonamide were found as lead molecules since they have exhibited promising activity against both the cancer cell lines used in this study, whereas the compounds 4-(trifluoromethyl)-N-(3-isopropoxy-1-isopropyl-4-(3,5-dimethyl-2H-pyrrol-2-yl)-1H-pyrazol-5-yl)benzamide and 2,6-difluoro-N-(3-isopropoxy-1-isopropyl-4-(3,5-dimethyl-2H-pyrrol-2-yl)-1H-pyrazol-5-yl) benzamide were found to be active against the pancreatic cell line only. Rest all the other compounds were found to exhibit moderate to good activity towards both the cell lines.

(E)-9-oxooctadec-10-en-12-ynoic acid from Ixora brachiata Roxb. increases glucose uptake in L6 myotubes by activating the PI3K pathway.

In an attempt to discover new scaffolds for anti-diabetic activity from plants, we screened extracts from Ixora brachiata Roxb. for their effect on glucose uptake in L6 myotubes. The petroleum (PE) extract of the plant showed a significant increase in insulin stimulated glucose uptake by L6 myotubes. The bioactivity guided fractionation of the crude extract yielded a compound (E)-9-oxooctadec-10-en-12-ynoic acid (OEA). The compound induced a dose dependent increase in insulin stimulated glucose uptake in L6 myotubes with an EC50 of 22.96µM. OEA also increased the phosphorylation of IRS-1, Akt and AS160 leading to increased GLUT4 translocation to the plasma membrane indicating that it promotes insulin stimulated glucose uptake in L6 myotubes by activating the PI3K pathway.

Design, synthesis and biological evaluation of novel azaspiro analogs of linezolid as antibacterial and antitubercular agents.

The design, synthesis and antimicrobial evaluation of a novel series of azaspiro analogues of linezolid (1) have been described. Linezolid comprises of a morpholine ring which is known for its metabolism-related liabilities. Therefore, the key modification made in the linezolid structure was the replacement of morpholine moiety with its bioisostere, 2-oxa-6-azaspiro[3.3]heptane. Furthermore, the replacement of N-acetyl terminal of 1 with various aromatic or aliphatic functionalities was carried out. The title compounds were evaluated against a panel of Gram-positive and Gram-negative bacteria and Mycobacterium tuberculosis. Subsequent structure-activity relationship (SAR) studies identified several compounds with mixed antibacterial and antitubercular profiles. Compound 22 (IC50 0.72, 0.51, 0.88, 0.49 µg/mL for Escherichia coli, Pseudomonas aeruginosa, Staphylococcus aureus, Bacillus subtilis, respectively) exhibited similar antibacterial profile as 1. The N-acetyl derivative 18 was similar to 1 in antitubercular profile. Thus, the present study successfully demonstrated the use of azaspiro substructure in the medicinal chemistry of antibacterial and antitubercular agents.