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.

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.

Gender Disparity of First Authors in Review Article Publications Related to Schizophrenia

Schizophrenia is a severe psychotic condition that can be diagnosed when certain symptoms, such as disorganized speech, disorganized thoughts, or negative feelings, are present for at least six months in a person's life. Gender equity and representation in academic writing are significant issues that have received more attention recently. Understanding the gender discrepancies in authorship can help researchers studying schizophrenia overcome obstacles and potential biases. The purpose of this study was to determine the degree of gender discrepancy among initial authors of articles that focused on schizophrenia and to identify potential causes of such inequalities. A bibliometric analysis of articles related to schizophrenia published from 2019 to 2022 was conducted. The authors' genders were determined through available public records and professional affiliations. The analysis included assessing the proportion of male and female first authors and examining trends over time. A total of 982 articles were included in the analysis. The results revealed a significant gender disparity in first authorship, with a higher representation of male first authors (546, 55.6%) compared to female first authors (436, 44.4%). There is a significant increase in the percentage of female authors from 2019 to 2022 (i.e., from 25% to 48.5%). The number of female and male authors is predicted to remain at a constant from 2023 to 2027, with male authors at 175 and female authors at slightly above 150. The findings of this study underscore the need for continued efforts to address gender imbalances in academic publishing and promote gender equity in the field of schizophrenia research. Recognizing and rectifying these disparities can contribute to a more inclusive and diverse scientific community.

Microstructural evidence of reversal of PCOS by steroidal saponins of asparagus racemosus in PCOS induced rats

The purpose of this study is to investigate the histological and hormonal observations in fructose-fed, letrozole-induced polycysticovarian- syndrome (PCOS) rats treated with various doses of extract of asparagus racemosus (EAR) and Steroidal saponin (SAPO). 48 female Wistar albino rats were divided into 8 groups, including Vehicle Control (VC); PCOS; EAR 400 mg/kg; SAPO 40 mg/kg; PCOS + EAR 200 mg/kg; PCOS + EAR 400 mg/kg; PCOS + SAPO 20 mg/ kg; PCOS + SAPO 40 mg/kg. PCOS group was administered letrozole at a concentration of 1 mg/kg dissolved in 1% CMC per oral(p.o.) once daily for 28 days. Along with these, rats were allowed free access of 10% fructose solution daily. Calculated dosages of EAR and SAPO were given with oral gavage for 30 days. During experimental period, vaginal smears were collected daily for estrus cycle determination. Rats were sacrificed and blood samples were collected for hormonal assay. Ovaries were removed to proceed with histopathological study. Slides were stained using hematoxylin and eosin (H&E) stains. When compared to the vehicle control group, PCOS ovaries had a higher incidence of ovarian cysts, incomplete luteinization, and a lower number of corpus lutea. Although serum estradiol, progesterone, and Follicle-stimulating hormone (FSH) levels were lower in the PCOS group, testosterone and luteinizing hormone (LH) levels were higher. The findings of this study indicated that taking EAR 400 mg/kg and SAPO 40mg/kg orally could alleviate PCOS-related symptoms. It appears that consuming SAPO 40mg/kg reduces LH and testosterone levels while increasing FSH, estrogen, and progesterone hormone levels. Because of the hormonal balancing nature of these drugs, EAR 400mg/kg- and SAPO 40mg/ kg-treated rats had a lower number of cystic follicles and a higher number of corpora lutea. In PCOS rats, this results in a normal process of folliculogenesis and ovulation. In the current study, we observed that SAPO 40mg/kg is better compared to EAR 400mg/kg treatment.

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.

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.

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.

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.

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.

Modified fuzzy-based greedy routing protocol for VANETs

Vehicular Ad Hoc Networks (VANETs) is the most growing research area in wireless communication and has been gaining significant attention over recent years due to its role in designing intelligent transportation systems. Wireless multi-hop forwarding in VANETs is challenging since the data has to be relayed as soon as possible through the intermediate vehicles from the source to destination. This paper proposes a modified fuzzy-based greedy routing protocol (MFGR) which is an enhanced version of fuzzy logic-based greedy routing protocol (FLGR). Our proposed protocol applies fuzzy logic for the selection of the next greedy forwarder to forward the data reliably towards the destination. Five parameters, namely distance, direction, speed, position, and trust have been used to evaluate the node’s stability using fuzzy logic. The simulation results demonstrate that the proposed MFGR scheme can achieve the best performance in terms of the highest packet delivery ratio (PDR) and minimizes the average number of hops among all protocols.

A cognitive model for adopting ITIL framework to improve IT services in Indian IT industries

This research paper presents a cognitive model which manages to minimize the issues of the Information Technology Infrastructure by incorporation of service management practices. The importance of this research is that this model can be replicated in other companies for the distribution of products that wish to implement improvements in their management process technological services. This work introduces the use of Information Technology Infrastructure Library or ITIL as best practice, essential methodologies for IT Management, historical evolution, methodology, service life cycle, and ITIL certifications. Service automation is widely regarded as the usefulness and improves service guarantee. One of the most useful features of automation services is that the process will run the same way every time. Such precision in the execution of repetitive executions is virtually impossible when it comes to human labor. Therefore, the automation is the best way to improve the efficiency of the service provider and the next steps of the process.

Deep learning based intelligence cognitive vision drone for automatic plant diseases identification and spraying

The agriculture industry is of great importance in many countries and plays a considerable role in the national budget. Also, there is an increased interest in plantation and its effect on the environment. With vast areas suitable for farming, countries are always encouraging farmers through various programs to increase national farming production. However, the vast areas and large farms make it difficult for farmers and workers to continually monitor these broad areas to protect the plants from diseases and various weather conditions. A new concept dubbed Precision Farming has recently surfaced in which the latest technologies play an integral role in the farming process. In this paper, we propose a SMART Drone system equipped with high precision cameras, high computing power with proposed image processing methodologies, and connectivity for precision farming. The SMART system will automatically monitor vast farming areas with precision, identify infected plants, decide on the chemical and exact amount to spray. Besides, the system is connected to the cloud server for sending the images so that the cloud system can generate reports, including prediction on crop yield. The system is equipped with a user-friendly Human Computer Interface (HCI) for communication with the farm base. This multidrone system can process vast areas of farmland daily. The Image processing technique proposed in this paper is a modified ResNet architecture. The system is compared with deep CNN architecture and other machine learning based systems. The ResNet architecture achieves the highest average accuracy of 99.78% on a dataset consisting of 70,295 leaf images for 26 different diseases of 14 plants. The results obtained were compared with the CNN results applied in this paper and other similar techniques in previous literature. The comparisons indicate that the proposed ResNet architecture performs better compared to other similar techniques.

Uncovering hidden community structures in evolving networks based on neighborhood similarity

Detection of densely interconnected nodes also called modules or communities in static or dynamic networks has become a key approach to comprehend the topology, functions and organizations of the networks. Over the years, numerous methods have been proposed to detect the accurate community structure in the networks. State-of-the-art approaches only focus on finding non-overlapping and overlapping communities in a network. However, many networks are known to possess a hidden or embedded structure, where communities are recursively grouped into a hierarchical structure. Here, we reinvent such sub-communities within a community, which can be redefined based on nodes similarity. We term those derived communities as hidden or hierarchical communities. In this work, we present a method called Hidden Community based on Neighborhood Similarity Computation (HCNC) to uncover undetected groups of communities that embedded within a community. HCNC can detect hidden communities irrespective of density variation within the community. We define a new similarity measure based on the degree of a node and it’s adjacent nodes degree. We evaluate the efficiency of HCNC by comparing it with several well-known community detectors through various real-world and synthetic networks. Results show that HCNC has better performance in comparison to the candidate community detectors concerning various statistical measures. The most intriguing findings of HCNC is that it became the first research work to report the presence of hidden communities in Les Miserables, Karate and Polbooks networks.

Artificial Neural Network based SVPWM for Five Level Cascaded H-bridge Inverter fed Grid connected PV System

An Artificial Neural Network (ANN) based Space Vector Pulse Width Modulation (SVPWM) for five level cascaded H-bridge inverter (CHBI) fed grid connected photovoltaic (PV) system. The multilevel inverter topologies are offers better performance compare conventional two level inverter like reduced total harmonic distortion, less electromagnetic interferences and voltage stresses across switches are low. The ANN based SVPWM generates the switching pulses for cascaded H-bridge inverter; it improves the accuracy in reference vectors tuning and identification, which leads to improve the inverter output voltage, better utilization of dc-link voltage and controlled output current. The ANN control makes the implementation of SVPWM becomes simple and minimizes the intricacy in tracking reference vector and calculation of switching time; it is suitable for any type of non-linear systems. This proposed system is energized using PV system and it is boosted using dc-dc boost converter, and the output of CHBI is synchronized with grid connected system using coupled inductor. The simulation and experimental results of ANN based SVPWM for CHBI is verified using simulink-matlab and DSP processor.

Optimal broadcast scheduling method for VANETs: An adaptive discrete firefly approach

Vehicular Adhoc Networks (VANET) are thought-about as a mainstay in Intelligent Transportation System (ITS). For an efficient vehicular Adhoc network, broadcasting i.e. sharing a safety related message across all vehicles and infrastructure throughout the network is pivotal. Hence an efficient TDMA based MAC protocol for VANETs would serve the purpose of broadcast scheduling. At the same time, high mobility, influential traffic density, and an altering network topology makes it strenuous to form an efficient broadcast schedule. In this paper an evolutionary approach has been chosen to solve the broadcast scheduling problem in VANETs. The paper focusses on identifying an optimal solution with minimal TDMA frames and increased transmissions. These two parameters are the converging factor for the evolutionary algorithms employed. The proposed approach uses an Adaptive Discrete Firefly Algorithm (ADFA) for solving the Broadcast Scheduling Problem (BSP). The results are compared with traditional evolutionary approaches such as Genetic Algorithm and Cuckoo search algorithm. A mathematical analysis to find the probability of achieving a time slot is done using Markov Chain analysis.

Fuzzy rule based SLA generation algorithms for web based multi party negotiation systems

Service Level Agreement (SLA) is an agreement between the service provider and consumer to provide the verifiable quality of services. Using the valuable metrics in SLA, a service consumer could easily evaluate the service provider. Though there are different types of SLA models are available between the consumer and provider, the proposed approach describes the Fuzzy rule base SLA agreement generation among multiple service providers. A negotiation system is designed in this work to collect the different sets of provider services. With their desired quality metrics, a common Fuzzy based SLA report is generated and compared against the existing consumer requirements. From the analysis of the common agreement report, consumers can easily evaluate the best service with the desired Impact service, cost and Quality. The main advantage of this approach is that it reduces the time consumption of a consumer. Moreover, the best service provider can be selected among multiple providers with the desired QoS parameters. At the same time, the bilateral negotiation is enhanced with the approach of multilateral negotiation to improve the searching time of consumers.

White blood cell detection and classification using Euler’s Jenks optimized multinomial logistic neural networks

Due to the wide acceptance of White Blood Cells (WBCs) in disease diagnosis, detection and classification of WBC are hot topic. Existing methodologies have some drawbacks such as significant degree of error, higher accuracy, time bound and higher misclassification rate. A WBCs detection and classification called, Jenks Optimized Logistic Convolutional Neural Network (JO-LCNN) method has proposed. Initally, Eulers Principal Axis is used as a convolution model to obtain a rotation invariant form of image by differentiating the background and RBCs, then eliminating them which leaves only the WBCs. By eliminating the wanton features, inherent features are detected contributing to minimum misclassification rate. According to above, Jenks Optimization function is used as a pooling model to obtain feature map for lower resolution. Therefore JO-LCNN is used for removing tiny objects in image and complete nuclei. Finally, Multinomial Logistic classifier is used to classify five types of classes by means of loss function and updating weight according to the loss function, therefore classifying with higher accuracy rate. Using LISC database for WBCs with different parameters as classification accuracy, false positive rate and time complexity are performed. Result shows that JO-LCNN, efficiently improves accuracy with less time, misclassification rate than the state-of-art methods.

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.