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Parkinson’s disease (PD) is a neurodegenerative disorder characterized by several motor symptoms such as resting tremor, muscular rigidity, slowness of movement and different speech impairments. PD is a kind of singular, multi-system disorder that gradually worsen over the time. In this research, we classify the neurological state of the patients with Parkinson’s disease (PwPD) according to the third section of the Movement Disorders Society - Unified Parkinson’s Disease Rating Scale (MDS-UPDRS-III) using multimodal bio-signal data. As PD advances from low to advanced state, PwPD finds it difficult in their speech production and irregularities in their gait patterns. Monitoring the chaotic nature of time series data corresponding to speech and gait biomarkers can provide insights into the progression of the condition across different stages. This work for the first time analyze PD in a complex system perspective while representing the biomarkers as complex networks. The time-series corresponding to speech and gait signals are represented separately, as a complex network using the visibility graph algorithm. The characterization of the different stages of PD is explored for each modalities using different network features. Performance evaluation shows that the results obtained using the multimodal configuration of speech and gait left foot signals outperform the state-of the-art method. Moreover, performance comparison with the unimodal counterparts proves the need for multimodal assessment of PD severity. The configuration ‘speech and gait left foot’ outperforms (in terms of accuracy) that of the unimodal by 32% in speech, 3% in gait left foot, 19% in gait right foot, and 3% in gait both feet.

Introduction: Diabetic foot ulcers are critical complications of uncontrolled diabetes, with peripheral neuropathy, peripheral arterial disease, and immune suppression as primary contributing factors. Adenosine Deaminase (ADA), an enzyme, has been identified as a promising marker for cell-mediated immunity. Its estimation may serve as a predictive marker for immunomodulation, which, in turn, leads to complications among individuals with diabetes. Aim: To estimate serum ADA levels in diabetic patients without diabetic foot and diabetic patients with diabetic foot. Additionally, the study aimed to investigate the association of ADA serum levels with glucose, Glycated Haemoglobin (HbA1c), and the severity of wounds. Materials and Methods: This cross-sectional study was conducted in the central laboratory Department of Biochemistry at Krishna Rajendra Hospital (KR Hospital), Mysuru Medical College and Research Institute, Mysuru, India, from August to September 2021. The study included diabetic foot cases (n=45) and Type 2 diabetes patients on regular follow-up in the Department of Medicine without diabetic foot and any other complications (n=45). Venous blood samples were analysed for ADA, glucose, HbA1c, and complete blood count. The data were statistically analysed using the Mann-Whitney U test, Kruskal-Wallis test, and Spearman rank correlation test. Results: The present study observed a higher incidence of diabetic foot among males. It showed a statistically significant increase in serum ADA, HbA1C, and neutrophil count in patients with diabetic foot compared to patients with diabetes without diabetic foot ulcers, with mean Standard Deviation (SD) of 71.79±25.11, 11.53±3.11%, and 75.91±9.77%, respectively. Serum ADA exhibited a positive correlation with HbA1C and different grades of ulcers. Conclusion: The present study demonstrated a significantly increased serum ADA in diabetic ulcers and a positive correlation with different grades of ulcers. This finding contributes to a better understanding of the disease's pathogenesis at different stages. Furthermore, the positive correlation between ADA and HbA1C levels may play an important role in predicting the glycaemic and immunological status of these patients.

Abstract Public imaging datasets are critical for the development and evaluation of automated tools in cancer imaging. Unfortunately, many do not include annotations or image-derived features, complicating downstream analysis. Artificial intelligence-based annotation tools have been shown to achieve acceptable performance and can be used to automatically annotate large datasets. As part of the effort to enrich public data available within NCI Imaging Data Commons (IDC), here we introduce AI-generated annotations for two collections containing computed tomography images of the chest, NSCLC-Radiomics, and a subset of the National Lung Screening Trial. Using publicly available AI algorithms, we derived volumetric annotations of thoracic organs-at-risk, their corresponding radiomics features, and slice-level annotations of anatomical landmarks and regions. The resulting annotations are publicly available within IDC, where the DICOM format is used to harmonize the data and achieve FAIR (Findable, Accessible, Interoperable, Reusable) data principles. The annotations are accompanied by cloud-enabled notebooks demonstrating their use. This study reinforces the need for large, publicly accessible curated datasets and demonstrates how AI can aid in cancer imaging.

The geographical exchange of non-native species can be highly asymmetrical, with some world regions donating or receiving more species than others. Several hypotheses have been proposed to explain such asymmetries, including differences in propagule pressure, source species (invader) pools, environmental features in recipient regions, or biological traits of invaders. We quantified spatiotemporal patterns in the exchange of non-native insects between Europe, North America, and Australasia, and then tested possible explanations for these patterns based on regional trade (import values) and model estimates of invader pool sizes. Europe was the dominant donor of non-native insect species between the three regions, with most of this asymmetry arising prior to 1950. This could not be explained by differences in import values (1827–2014), nor were there substantial differences in the sizes of modelled invader pools. Based on additional evidence from literature, we propose that patterns of historical plant introductions may explain these asymmetries, but this possibility requires further study.

Early cancer identification using gene expression data is critical for providing successful patient care. Accurate data recognition is essential to prevent improper detection because it may result in higher complexities and increased mortality rates. Gene expression data typically include numerous features, each representing distinct genes. The abundance of features introduces high dimensionality, contributing to high computational complexity and resource demands. Furthermore, the presence of redundant or highly correlated selected features may lead to multicollinearity issues. In the existing works, certain limitations, such as reduced performance due to degraded data quality, high storage space requirements, overfitting issues, and lack of robustness, can compromise overall classification accuracy. To address these challenges and enhance classification outcomes, this research employs an efficient framework based on a deep learning (DL) approach. Initially, the data is collected from five gene cancer datasets, which are then augmented to maximize the data size. Min-Max Normalization is used for data pre-processing. The Enhanced Chimp Optimization (ECO) algorithm is applied to select the most significant genes while eliminating redundant or unwanted ones. Based on the selected gene set, the Depth-wise Separable Convolutional Neural Network (DSCNN) is employed to categorize diverse cancerous and non-cancerous classes. The performance of the proposed model is greatly improved by resolving the dimensionality and overfitting problems. The implementation is carried out using PYTHON, and the overall accuracy of the DSCNN model is determined to be 99.18% across all datasets. Additionally, metrics such as precision, recall, and F1 score are evaluated to analyze the model’s overall performance. The proposed model demonstrates a significantly higher effective performance compared to existing approaches.

Background: Tuberculosis (TB) remains a prominent global health challenge, distinguished by substantial occurrences of infection and death. The upsurge of drug-resistant TB strains underscores the urgency to identify novel therapeutic targets and repurpose existing compounds. Rv0295c is a potentially druggable enzyme involved in cell wall biosynthesis and virulence. We evaluated the inhibitory activity of Food and Drug Administration (FDA)-approved compounds against Rv0295c of Mycobacterium tuberculosis, employing molecular docking, ADME evaluation, and dynamics simulations. Methods: The study screened 1800 FDA-approved compounds and selected the top five compounds with the highest docking scores. Following this, we subjected the initially screened ligands to ADME analysis based on their dock scores. In addition, the compound exhibited the highest binding affinity chosen for molecular dynamics (MD) simulation to investigate the dynamic behavior of the ligand–receptor complex. Results: Dihydroergotamine (CHEMBL1732) exhibited the highest binding affinity (−12.8 kcal/mol) for Rv0295c within this set of compounds. We evaluated the stability and binding modes of the complex over extended simulation trajectories. Conclusion: Our in silico analysis demonstrates that FDA-approved drugs can serve as potential Rv0295c inhibitors through repurposing. The combination of molecular docking and MD simulation offers a comprehensive understanding of the interactions between ligands and the protein target, providing valuable guidance for further experimental validation. Identifying Rv0295c inhibitors may contribute to new anti-TB drugs.

Fronthaul data rates in case of millimeter wave (mmWave) wireless systems scale up to several Gbps in the currently deployed digitized radio over fiber solutions, resulting in large complexity remote radio heads (RRHs). We propose a promising solution involving optical frequency doubling and quadrupling, where all complex digital processing are moved to the centralized base band unit (BBU), thereby allowing placement of large number of low-complex RRHs at long distances. We experimentally demonstrate optical frequency doubling and quadrupling for the generation and transport of millimeter wave mmWave signals with OFDM containing QPSK, 16/64/256-QAM modulation formats over a distance of 2 km. These schemes enable the use of low-frequency RF signals at the BBU for the generation of mmWaves at the RRH. We evaluate the performance of the systems in terms of error vector magnitude (EVM) and out-of-band emissions of the upconverted data. Our results show that the proposed schemes achieve EVM values within the 3GPP 5G standard requirements. We also investigate the tunability and scalability of the proposed schemes across the n258 band (24.25 - 27.5 GHz) and discuss the feasibility of implementing dense wavelength division multiplexing (DWDM) for servicing multi-channel operation required for multisector antennas. The proposed schemes can support high data rate transmission for future wireless networks including 6G.

Distribution systems are evolving from traditional passive networks into, what is known as, Active Distribution Networks (ADNs). Unlike traditional distribution networks, ADNs are characterized by bi-directional power flow, the high penetration of DERs, storage capabilities and sophisticated control strategies. Multiple layers of communications, sensing and computation are being integrated into ADNs for monitoring, control and protection of a variety of components and critical operations. This enhanced dependency on information and communication technologies, however, increases the exposure of ADNs to cyber-attacks. Several papers have been published in recent years with a focus on cyber-physical security (CPS) of smart grids. However, the published survey papers primarily emphasize the transmission level of smart grid threats and challenges, with little focus on the ADNs. Given the rapid deployment of ADNs and the increasing cyber threats against power grids and critical infrastructures, we are motivated, in this article, to present a review and survey focused, instead, on the latest research advancements in the area of CPS for ADNs. This paper represents the first survey of timely research in the area of CPS of ADNs with a focus on ADN critical operations and components. The cyber-physical aspects of each critical operation/component are analyzed. In addition, the challenges and requirements of associated communication protocols and standards are presented. Cybersecurity of ADN devices and sensors including Phasor Measurement Units (PMUs), smart meters, advanced metering infrastructure and protection relays are discussed in detail. Moreover, a thorough study of ADNs application drivers and enablers including microgrids, Electric Vehicles (EVs), Internet-of-Things (IoT) and smart homes is conducted. Potential and existing solutions by industry are highlighted. Finally, survey outcomes and directions for future work are presented to highlight emerging avenues of research.

Decentralized clinical trials (DCTs) are gaining momentum in clinical research as these studies can be executed remotely through telemedicine and mobile/local health-care providers. The COVID-19 pandemic has further accelerated advances and adoption in this area. In the past few years, there has been significant development and growth in the use of remote data that are electronically transmitted from a clinical trial (CT) participant, from outside the clinical setting, to a data repository. Such data may include laboratory data, safety data, or outcome measures reported by the participant, the clinician or the observer. Similarly, wearable health monitoring devices are being increasingly used in health-care and CT settings. Digital biomarkers, which can support continuous measurement of physiologic parameters outside the physical confines of the clinical environment, are also creating new and improved opportunities for patient care and biomedical research, enabling remote monitoring and DCTs. There are several benefits to using remote data capture, wearables, and digital biomarkers in clinical health-care research; however, several questions and challenges still need to be addressed. In an effort to understand the adoption of these technologies in DCTs, and the challenges therein, the authors of this workstream conducted an online survey of clinical research stakeholders across India and reviewed 80 responses. The review article summarizes the key findings from this online survey.

With the increasing use of internet and mobile phones, the capacity of the backbone optical communication link has been continually growing across the world and especially in India. The total optical fiber cable deployed for the BharatNet initiative of Government of India is expected to increase from 3.4 million km to 5 million km in 2024–25 just for providing last-mile connectivity. Considering this deep proliferation, this article attempts to capture the diverse research carried out in India in the domain of optical communication.

Abstract Due to the paucity of longitudinal molecular studies of COVID-19, particularly those covering the early stages of infection (Days 1-8 symptom onset), our understanding of host response over the disease course is limited. We perform longitudinal single cell RNA-seq on 286 blood samples from 108 age- and sex-matched COVID-19 patients, including 73 with early samples. We examine discrete cell subtypes and continuous cell states longitudinally, and we identify upregulation of type I IFN-stimulated genes (ISGs) as the predominant early signature of subsequent worsening of symptoms, which we validate in an independent cohort and corroborate by plasma markers. However, ISG expression is dynamic in progressors, spiking early and then rapidly receding to the level of severity-matched non-progressors. In contrast, cross-sectional analysis shows that ISG expression is deficient and IFN suppressors such as SOCS3 are upregulated in severe and critical COVID-19. We validate the latter in four independent cohorts, and SOCS3 inhibition reduces SARS-CoV-2 replication in vitro. In summary, we identify complexity in type I IFN response to COVID-19, as well as a potential avenue for host-directed therapy.

Background: Female pattern hair loss (FPHL) is one of the hair loss disorders in women characterized by a gradual reduction in hair from the frontal, central, and parietal areas of the scalp. FPHL is more common than previously thought, and data regarding the same are lacking in the Indian literature. Unlike male pattern hair loss, there are not much studies regarding FPHL in South India. Hence, we intend to study the clinical features, associations, and dermoscopic findings of FPHL in our population. Aims and Objectives: The study was conducted to estimate the clinical and dermoscopic features of FPHL. The secondary objective of the study was to estimate the severity of FPHL based on clinical and dermoscopic features. Materials and Methods: The cross-sectional descriptive study was conducted in 300 patients who were clinically diagnosed with FPHL attending the Department of Dermatology, Government Medical College, Kottayam, during a period of 18 months from February 2021 to August 2022. Results: The mean age of patients with FPHL was 46.99±13.8 years. The majority of the patients were asymptomatic. Hair diameter diversity of >20% and miniaturized hair were the most common dermoscopic findings seen in all patients. Honeycombing and yew dots in dermoscopy were seen in 17 and 8.4% of patients, respectively. Conclusion: Dermoscopes are a useful tool to differentiate FPHL from other causes of diffuse hair loss that could mimic FPHL. The presence of yellow dots and honeycombs indicated advanced FPHL. Thus, dermoscopy helps in detecting early FPHL and initiating early treatment of this condition.

Background and aim: Novel nature of the viral pathogen SARS-CoV-2 and the absence of standard drugs for treatment, have been a major challenge to combat this deadly infection. Natural products offer safe and effective remedy, for which traditional ethnic medicine can provide leads. An indigenous poly-herbal formulation, Kabasura Kudineer from Siddha system of medicine was evaluated here using a combination of computational approaches, to identify potential inhibitors against two anti-SARS-CoV-2 targets – post-fusion Spike protein (structural protein) and main protease (Mpro, non-structural protein). Experimental procedure: We docked 32 phytochemicals from the poly-herbal formulation against viral post-fusion Spike glycoprotein and Mpro followed by molecular dynamics using Schrodinger software. Drug-likeness analysis was performed using machine learning (ML) approach and pkCSM. Results: The binding affinity of the phytochemicals in Kabasura Kudineer revealed the following top-five bioactives: Quercetin > Luteolin > Chrysoeriol > 5-Hydroxy-7,8-Dimethoxyflavone > Scutellarein against Mpro target, and Gallic acid > Piperlonguminine > Chrysoeriol > Elemol > Piperine against post-fusion Spike protein target. Quercetin and Gallic acid exhibited binding stability in complexation with their respective viral-targets and favourable free energy change as revealed by the molecular dynamics simulations and MM-PBSA analysis. In silico predicted pharmacokinetic profiling of these ligands revealed appropriate drug-likeness properties. Conclusion: These outcomes provide: (a) potential mechanism for the anti-viral efficacy of the indigenous Siddha formulation, targeting Mpro and post-fusion Spike protein (b) top bioactive lead-molecules that may be developed as natural product-based anti-viral pharmacotherapy and their pleiotropic protective effects may be leveraged to manage co-morbidities associated with COVID-19.

Internet of Things (IoT) edge devices are becoming extremely popular because of their ability to process data locally, conserve bandwidth, and reduce latency. However, with the developing count of IoT devices, threat detection, and security are becoming major concerns. IoT edge devices must avoid cyber threats and protect user data. These devices frequently take limited resources and can run on lightweight operating systems, which makes them vulnerable to security attacks. Intrusion detection systems (IDS) can be run on edge devices to recognize suspicious actions and possible risks. These systems monitor traffic patterns, and behavior, and identify attack signatures to detect and report on possible attacks. This study presents a design for an inverse chi square-based flamingo search optimization algorithm with machine learning (ICSFSO-ML) as a security solution for Internet of Things edge devices. The goal of the ICSFSO-ML technique is to apply ML and metaheuristics for threat recognition in IoT edge devices. To reduce the high dimensionality problem, the ICSFSO-ML technique uses the ICSFSO algorithm for feature selection purposes. Further, the ICSFSO-ML technique exploits the stacked bidirectional long short-term memory (SBiLSTM) model for the threat detection process. To enhance the efficacy of the SBiLSTM model, an arithmetic optimization algorithm (AOA) is applied for the hyperparameter selection process. The simulation performance of the ICSFSO-ML technique can be tested on a benchmark threat database. The performance analysis showed the benefits of the ICSFSO-ML methodology compared to existing methodologies with a maximum accuracy of 98.22%.

This study aims to analyse the relationship between Corporate Social Responsibility (CSR) and Corporate Financial Performance (CFP) of Indian firms with the moderating role of CSR strategic integration on the relationship. This study examined a sample of 134 manufacturing firms listed on the National Stock Exchange of India from 2011 to 2021. The authors used a random effects panel regression model to study how CSR strategic integration affects the CSR-CFP relationship. The findings of this study show a significantly negative impact of CSR on CFP. Regarding the moderating effect, a positive interaction of CSR strategic integration is found, which means that the relationship between CSR and CFP is strengthened when CSR is undertaken by businesses that consider the goals of a firm. The study suggests that different measures of CSR strategic integration can be used in future studies, and market-based financial performance measures can be additionally used to test these relationships. The study contributes to the literature concerning CSR-CFP relationship by taking CSR strategic integration as the moderating variable, which is a novel idea. This study also provides suggestions to companies and policymakers who can incorporate strategic considerations in the business case of CSR, which will create positive outcomes for both companies and society.

Liver cancer ranks as the third most common cause of cancer-related death rate, resulting in an estimated 830,180 deaths globally. Recent, 2023 estimation of the American Cancer Society predicted around 29,380 deaths due to this disease in America alone. However, with early detection and proper drug management the survival rate can be greatly extended. Significantly, the noteworthy observation pertains to the absence of consistent drug responses among patients with identical cancer types at the same stage. Hence the need of the hour is the precision medicine that predict patient specific drug response based on molecular data genome expression. In this regard, we propose drug response system that uses deep learning framework that predicts drug response in new cell lines or patient with the aim of accurate prediction and suggesting précised medicine to that particular cancer patient. The prediction of the framework is on the lines of recommended system’s projection of drugs and cell lines into latent pharmacogenomic space. During our experimentation, we utilized the Genomics of Drug Sensitivity in Cancer dataset and observed that Cancer Drug Response and Patient Specific Recommender System (CDRP-PRS) consistently delivers robust predictions and effective response, even when applied to previously unseen patient-derived cell line datasets. CDRP-PRS inferences in the pharmacogenomic space are helpful to understand the drug mechanism to identify the cellular subtypes and their categorized associations. Further it is aimed is to predict the accuracy of drug responses for patients and suggest precise medications tailored to their specific cancer.

In the applications of computer vision and pattern recognition, facial image processing has been a great issue to focus on for providing efficient solutions for face recognition. General face recognition models can be classified into two types, geometry-based and appearance-based feature models, which deal with global feature data and facial textures respectively. Normally the performance of an adaptive face detection model increases with an increase in the number of training images. In this study, a novel model called Local Symmetrical Patterns based feature extraction model (LSP-FEM) for efficient face recognition was developed. The model incorporates Local Symmetrical Patterns (LSP) to recognize the input human facial samples. Moreover, the proposed LSP-FEM computes the symmetry of each pixel in all eight directions of facial images. For an efficient recognition process, a facial image is considered as a collection of LSP codes. Furthermore, the experimentation was carried out using benchmark datasets called the FERET dataset, Extended Yale-B dataset and Olivetti Research Laboratory (ORL) dataset images. The results show that the accuracy rate of face recognition is higher than that of the existing models.

The current era of rapid climate change necessitates greater emphasis on wild, often underutilized yet sturdy, edible plants that are capable of growing in harsh arid lands. When compared to more popular crops like rice, these are often of traditional significance and more region-specific; but needing less chemical fertilizers, pesticides and irrigation water, they can not only provide food and nutrition in a sustainable manner but also medicinally valuable compounds (nutraceuticals) to target various communicable and non-communicable diseases. These bioactive metabolites could also serve as markers for in-process quality control of herbal formulations and as metabolic biomarkers. Of late, a few of the common food crops across the world have benefited from the use of technological interventions, employing various Internet of Things (IoT) devices and sensors to collect data on the farm and conduct agro-food specific analytics. Machine Learning (ML) and deep learning (DL) have found application in numerous facets of agriculture, particularly in tasks such as yield prediction, disease detection, weed detection, crop recognition, and assessing crop quality at pre-harvest, harvest, and post-harvest stages. ML technology also has shown potential to be effectively employed at various stages of bioactives discovery, encompassing target identification, compound screening, lead discovery, as well as pre-clinical and clinical development phases. However, the usage of these modern technologies has been less explored in the desert plants of the world. The current article reviews a few available examples and highlights the potential of employing ML and DL technologies in edible plants of the world, with a focus on sustainable desert flora, for achievement of multidisciplinary objectives, that is, agro-food production, food safety and bioactives discovery.

ABSTRACTAlphaviruses are arthropod-borne, single-stranded positive-sense RNA viruses that are recognized as rapidly emerging pathogens. Despite being exquisitely sensitive to the effects of the innate immune response alphaviruses can readily replicate, disseminate, and induce pathogenesis in immunologically competent hosts. Nonetheless, how alphaviruses evade the induction of an innate immune response prior to viral gene expression, or in non-permissive infections, is unknown. Previously we reported the identification of a novel host/pathogen interaction between the viral Capsid (CP) protein and the host IRAK1 protein. The CP/IRAK1 interaction was determined to negatively impact IRAK1-dependent PAMP detection in vitro, however, the precise importance of the CP/IRAK1 interaction to alphaviral infection remained unknown. Here we detail the identification of the CP/IRAK1 interaction determinants of the Sindbis virus (SINV) CP protein and examine the importance of the interaction to alphaviral infection and pathogenesis in vivo using an interaction deficient mutant of the model neurotropic strain of SINV. Importantly, these interaction determinants are highly conserved across multiple Old-World alphaviruses, including Ross River virus (RRV), Mayaro virus (MAYV), Chikungunya virus (CHIKV), and Semliki Forest virus (SFV). In the absence of a functional CP/IRAK1 interaction, SINV replication is significantly restricted and fails to disseminate from the primary site of inoculation due to the induction of a robust type-I Interferon response. Altogether these data indicate that the evasion of IRAK1-dependent signalling is critical to overcoming the host innate immune response and the in vivo data presented here demonstrate the importance of the CP/IRAK1 interaction to neurovirulence and pathogenesis.

AbstractHandwritten character recognition (HCR) is a critical component of diverse applications, especially in multilingual regions such as India. Despite technological advancements, recognizing handwritten characters remains challenging because of variations in writing style, shape and linguistic nuances. This study presents an efficient HCR system tailored for Indian languages, such as Tamil and Telugu scripts, to address these challenges. The proposed nearest quad-tree (NQT) classifier enhances recognition accuracy while ensuring computational efficiency. This novel approach, a light-weight design, combines local feature-level decisions with a global decision-making process, thereby making it suitable for devices with limited computational resources. Key contributions include the emphasis of the NQT classifier on efficiency and lightweight design, applicability to regional languages and usability in resource-constrained settings. This study utilized three datasets from the Tamil, Devanagari and Telugu languages to demonstrate the exceptional performance of the NQT classifier in character recognition through the minimization of misclassification errors. The classifier exhibited consistently low misclassification error rates across a diverse range of characters, outperforming existing deep learning models. Notably, the NQT classifier achieved an overall accuracy, precision, recall and F1-measure of 96%, 95%, 97% and 96%, respectively, across the three datasets, surpassing the performance of popular transfer learning models. Furthermore, a comprehensive computational efficiency analysis highlighted the superior efficiency of the NQT classifier in terms of training time, CPU and GPU utilization and memory requirements, underscoring its potential for practical applications in resource-constrained environments.

Objective: Standing desks present a novel approach to reduce sedentary time in the classroom and address cardiovascular risk factors at an early age. In the context of designing a standing desk study, parents and children were surveyed regarding their perceptions and current use of standing desks and other flexible seating. Methods: Survey administered from January 31st to February 26th, 2024 to a convenience cohort of 50 parent–child pairs presenting for well or acute care at a pediatrics clinic affiliated with an academic institution (Hershey, Pennsylvania, United States). Logistic regression examined parent support of and child willingness to use a standing desk in the classroom. Results: Parents were primarily non-Hispanic, white females above 40 years of age. Child participants mean age and grade level were 10.5 years and 5th grade respectively. Among parents, 85 % (39/46) were supportive of their child’s use of a standing desk in the classroom, with 4 declining to answer. For children, almost half, 48 % (24/50), were willing to use a standing desk. Acceptability decreased for child body mass index (BMI) ≥85th percentile versus BMI

Rationale: Human immunodeficiency virus (HIV) infection is associated with chronic lung disease and impaired pulmonary function; however, longitudinal pulmonary function phenotypes in HIV are undefined. Objectives: To identify pulmonary function trajectories, their determinants, and outcomes. Methods: We used data from participants with HIV in the Pittsburgh HIV Lung Cohort with three or more pulmonary function tests between 2007 and 2020. We analyzed post-bronchodilator forced expiratory volume in 1 second (FEV1), forced vital capacity (FVC), and FEV1/FVC, and diffusing capacity of the lung for carbon monoxide (DlCO) using group-based trajectory modeling to identify subgroups of individuals whose measurements followed a similar pattern over time. We examined the association between participant characteristics and trajectories using multivariable logistic regression. In exploratory adjusted analyses restricted to individuals with available plasma cytokine data, we investigated the association between 18 individual standardized cytokine concentrations and trajectories. We compared mortality, dyspnea prevalence, respiratory health status, and 6-minute-walk distance between phenotypes. Results: A total of 265 participants contributed 1,606 pulmonary function measurements over a median follow-up of 8.1 years. We identified two trajectories each for FEV1 and FVC: "low baseline, slow decline" and "high baseline, rapid decline." There were three trajectory groups for FEV1/FVC: "rapid decline," "moderate decline," and "slow decline." Finally, we identified two trajectories for DlCO: "baseline low" and "baseline high." The low baseline, slow decline FEV1 and FVC, rapid decline, and moderate decline FEV1/FVC, and baseline low DlCO phenotypes were associated with increased dyspnea prevalence, worse respiratory health status, and decreased 6-minute-walk distance. The baseline low DlCO phenotype was also associated with worse mortality. Current smoking and pack-years of smoking were associated with the adverse FEV1, FEV1/FVC, and DlCO phenotypes. Detectable viremia was the only HIV marker associated with the adverse DlCO phenotype. C-reactive protein and endothelin-1 were associated with the adverse FEV1 and FVC phenotypes, and endothelin-1 trended toward an association with the adverse DlCO phenotype. Conclusions: We identified novel, distinct longitudinal pulmonary function phenotypes with significant differences in characteristics and outcomes. These findings highlight the importance of lung dysfunction over time in people with HIV and should be validated in additional cohorts.

Guideline recommendation for a plant bioactive such as flavan-3-ols is a departure from previous recommendations because it is not based on deficiencies but rather improvement in health outcomes. Nevertheless, there is a rapidly growing body of clinical data reflecting benefits of flavan-3-ol intake that outweigh potential harms. Thus, the objective of the Expert Panel was to develop an intake recommendation for flavan-3-ols and cardiometabolic outcomes to inform multiple stakeholders including clinicians, policymakers, public health entities, and consumers. Guideline development followed the process set forth by the Academy of Nutrition and Dietetics, which includes use of the Evidence to Decision Framework. Studies informing this guideline (157 randomized controlled trials and 15 cohort studies) were previously reviewed in a recently published systematic review and meta-analysis. Quality and strength-of-evidence along with risk-of-bias in reporting was reviewed. In drafting the guideline, data assessments and opinions by authoritative scientific bodies providing guidance on the safety of flavan-3-ols were considered. Moderate evidence supporting cardiometabolic protection resulting from flavan-3-ol intake in the range of 400-600 mg/d was supported in the literature. Further, increasing consumption of dietary flavan-3-ols can help improve blood pressure, cholesterol concentrations, and blood sugar. Strength of evidence was strongest for some biomarkers (i.e., systolic blood pressure, total cholesterol, HDL cholesterol, and insulin/glucose dynamics). It should be noted that this is a food-based guideline and not a recommendation for flavan-3-ol supplements. This guideline was based on beneficial effects observed across a range of disease biomarkers and endpoints. Although a comprehensive assessment of available data has been reviewed, evidence gaps identified herein can inform scientists in guiding future randomized clinical trials.

STUDY OBJECTIVES: Although obesity, asthma, and sleep-disordered breathing are interrelated, there is limited understanding of the independent contributions of body-mass index and pulmonary function on polysomnography in children with asthma. METHODS: We conducted a retrospective chart review on 448 7- to 18-year-old children with asthma who had undergone polysomnography testing between 1/2007-12/2011 to elucidate the association between spirometry variables, body-mass index, and polysomnography parameters, adjusting for asthma and antiallergic medications. RESULTS: Obese children had poorer sleep architecture and more severe gas exchange abnormalities compared to healthy weight children. Multivariate analysis revealed an independent association of body-mass index with sleep efficiency, with more light and less deep sleep in both obese and healthy-weight children, and with baseline oxygen saturation and oxygen nadir in obese children. In obese children, forced vital capacity was independently associated with less deep sleep (time in N3 sleep) as well as with oxygen nadir, while among healthy-weight children, forced expiratory volume directly correlated but forced vital capacity inversely correlated with deep sleep. In obese children, inhaled corticosteroid was associated with baseline oxygen saturation, and montelukast was associated with lower end-tidal carbon dioxide. In healthy-weight children, inhaled corticosteroid was associated with arousal awakening index, and montelukast was associated with light sleep. Antiallergic medications were not independently associated with polysomnography parameters. CONCLUSIONS: Pulmonary function, body-mass index, and asthma medications have independent and differing influences on sleep architecture and gas exchange polysomnography parameters in obese and healthy-weight children with asthma. Asthma medications are associated with improved gas exchange in obese children and improved sleep architecture in healthy-weight children with asthma. CITATION: Conrad LA, Nandalike K, Rani S, Rastogi D. Associations between sleep, obesity, and asthma in urban minority children. J Clin Sleep Med. 2022;18(10):2377-2385.

The coronavirus disease 2019 (COVID-19) pandemic supported an investigation of ongoing challenges as to whether and how to make mathematics relevant to learners' lifeworlds. Given that COVID-19 created major disruptions in all learners' lives, we developed and taught tasks that attempted to make links between their experiences of the pandemic and disciplinary mathematical knowledge. We located our investigation in current debates about the extent to which disciplinary knowledge can be linked to learners' out-of-school experiences. We developed and analysed two tasks about COVID-19 that could support link-making and productive disciplinary engagement, and analysed one Grade 10 teacher teaching these tasks. We found that linking mathematics to learners' lifeworlds is both possible and extremely difficult in relation to task design and how the teacher mediates the tasks. In relation to task design, we argue that teachers cannot do it alone; they need to be supported by the curriculum and textbooks. In relation to mediation, we saw that teacher practices are difficult to shift, even in the best of circumstances. We articulate the complexities and nuances involved in bridging powerful knowledge and lived experience and thus contribute to debates on how to teach powerful knowledge in relation to learners' lifeworlds.

A rapid demographic transition to aging populations is imminent over the next decade, especially in Southeast Asian countries. An aging population presents a host of medical challenges, such as multimorbidity; chronic illnesses; and geriatric syndromes, such as frailty, falls, dementia, and incontinence. In order to tackle such issues efficiently, there is an urgent need to train future doctors in geriatric medicine. Aged care is a specialty in medicine that was not traditionally part of the undergraduate medical curriculum. With the changing demographic, aged care content needs to be integrated into the existing curriculum for undergraduate medical students. In this paper, we present an introductory novice level geriatric module that was developed for first-year medical undergraduate students in Monash University, Malaysia. The module consisted of collaborative active learning sessions on healthy aging, interaction with older adults from the community, and a simulated experiential activity. Feedback was collected from students on module completion. The immediate impact of the module was positive from the student responses. The findings from this study will be useful to inform the development and implementation of future educational interventions for aged care, as well as to support the establishment of a vertically integrated aged care curriculum, in order to prepare future doctors to provide healthcare for the ageing Asian population.

Background: Schools are essential public health partners for safeguarding students' health. Child Death Review (CDR) is one public health activity where collaboration with schools is integral for developing strategies to prevent child death but the degree of collaboration is unknown. This study assessed school participation in CDR and the prevalence of school problems in reviewed child death cases, comparing non-suicide and suicide-related deaths. Methods: Using the National Fatality Review-Case Reporting System, we created a dataset of school-aged children (5 to 20 years) whose death was reviewed from 2005 to 2017 and used frequencies, proportions, and chi-squared statistics on selected measures. Results: Educational representatives infrequently participated in CDRs (24.9%). School records were rarely accessed for reviewed deaths (5.2%). Less than half (41.2%) of reviewed deaths had any school information and of these, 35.5% of children were indicated as having problems in school. Compared with non-suicide deaths, a larger proportion of suicide deaths had school representatives attend CDRs (28.4% vs 24.1%, P < 0.0001), and access to educational records (4.2% vs 9.2%, P < 0.0001). Implications: Efforts are needed to address potential barriers to systems integration, including state policies and federal educational privacy laws. Conclusions: School participation in CDRs is lacking for both suicide and non-suicide deaths.

Purpose: As learning at the workplace is predominantly self-motivated, this study is aimed to identify and categorize the motivational drivers for working professionals to pursue self-directed learning (SDL) at the workplace. Design/methodology/approach: A total of 23 variables were identified as drivers for working professionals to initiate, pursue and sustain self-directed learning at the workplace through literature review, interviews and focused group discussions with senior leaders from the industry. The participants were a mix of senior professionals responsible for leading large teams in their organizations and those appointed as people development leaders in their respective organizations. A questionnaire was developed based on the identified drivers and administered online. Around 315 working professionals were surveyed. Structural equation modelling and confirmatory factor analysis (CFA) methods were used to verify the scale and assess its validity and reliability. Findings: The results suggested that SDL motivational drivers for working professionals can be categorized into two broad categories, namely extrinsic (14 drivers) and intrinsic (9 drivers). Further, extrinsic drivers were subdivided into three categories: job expectations (6 drivers), negative consequence (2 drivers) and positive reinforcement (6 drivers) based on the initial exploratory and subsequent confirmatory analysis. The results suggest that job expectations and positive reinforcement positively influence the intrinsic motivation of an individual to pursue SDL and negative consequences negatively impact the intrinsic motivation. Practical implications: The findings from the study will be useful for human resource practitioners, managers and e-Learning companies to draw strategies for building an SDL culture. It also supports the advancement of research in adult education and learning in the workplace. Originality/value: A new scale to measure motivation for SDL by employees at the workplace is proposed. There are no scales available to measure employee motivation for pursuing SDL at the workplace to the best of our knowledge.

This article addresses the pressing issue of food waste by focusing on the development of young adolescents' food waste literacy. The concept of "food waste literacy" (FWL), defined as the literacy for the judicious use of food, encompasses knowledge, attitudes, values, and behaviours related to food waste. The article presents a study that aimed to enhance FWL among young adolescents in a New Zealand school through a teacher-directed guided inquiry intervention. A mixed-methods approach, including a survey and interviews, explored students' food waste knowledge, attitudes, values, and behaviours. The findings underscore the potential of appropriate units of learning and curriculum design for developing FWL. Promoting food citizenship in students is central to addressing food waste issues through educational initiatives. ["Developing Student Food Waste Literacy" was written with Chris Eames and Philippa Hunter.]

Abstract BackgroundLabia minora length is used in classification systems and to determine labiaplasty candidacy, with shorter labia leading to nonsurgical recommendations. ObjectivesThe aim of the study was to investigate the correlation between labia length and symptomatology. MethodsPatients undergoing labiaplasty from January 2017 to May 2023 underwent chart review. Data collected included age, exposed, and total labia length. Patients completed a preoperative survey with possible scores from 0 to 13 to gauge complaints and symptoms. ResultsOut of 50 charts with complete data, the average age was 34. Exposed labia lengths were 10.1 mm (right) and 11.4 mm (left); total lengths from sulcus to edge measured 32.0 mm (right) and 33.4 mm (left). Survey scores averaged 6.5 (range, 2-11) median of 7. The correlation between exposed labia length and symptoms yielded Pearson correlation coefficient values (Rr2Rr2 ConclusionsThe correlation between a patient's exposed and total labia length and reported symptomatology is weak. Patients with longer labia can experience few symptoms, just as those with shorter labia can have a high degree of symptomatology. Rather than use labia length as a primary factor determining labiaplasty candidacy, the focus should be on patient-reported symptoms. Level of Evidence: 2

Aims To create a safe and effective induction programme for Higher Specialist Trainees (HST) at Nottinghamshire Healthcare NHS Foundation Trust. An effective induction improves trainees' satisfaction, they feel welcomed and valued. It improves patient safety, retention, and recruitment (GMC Report 2020). Methods Based on GMC report, published in 2020, a survey was developed locally and data for 2021 HST induction was collected using digital platform. Initial stakeholder analysis completed, and relevant parties were invited to share the results. Two key deliverables were identified after consultation, one was a dedicated induction programme for HST which was co-produced along with trainees and stakeholders. The other deliverable was updating the induction booklet. The proposed induction plan was implemented in August 2023, the survey was repeated to the new HST cohort following induction via digital platform. Results of the survey were analysed via mixed methods (qualitative & quantitative). Results The surveys conducted in 2021 and 2023 were compared and there was an increase in response rate from 50% to 64%. The domains were devised from GMC standards and assessed by if staff had received everything in the domain within a week of starting their placement and results evaluated using a t-test. Domain A is gaining access to places and system (keys, fobs, security passes, computers, ID badges, mobile phones, IT system). This significantly improved from 27% to 88% with a p-value of < 0.001. Domain B is physical orientation of the setting (staff facilities such as lockers, parking, library, and site layout). This significantly improved from 45% to 88% with a p-value of < 0.018. Domain C is gaining day to day knowledge (HR, rota, annual leave, study leave, pay-roll, mandatory training, e-expenses, and guardian of safe working). There was no significant change between 9% and 19% with a p-value of < 0.48. Domain D is an understanding of expectations (duties and responsibility during working hours, on-call, team introduction). This significantly improved from 9% to 69% with a p-value of < 0.002. HSTs were given the chance to add comments and the responses in 2023 were more positive “excellent induction compared to previous years” compared with 2021 when HSTs felt isolated and devalued “worst ever induction in whole career in NHS”. Conclusion Overall, the results of the 2023 survey showed considerable improvement in all the key areas of induction within one week of starting the placement. Domain C demonstrates a challenge still and needs further work.

Aims RCPsych Dean's Grand Rounds focuses on understanding a problem or an opportunity for change in clinical practice using a patient story, academic evidence, and contextual data in this area to bridge the evidence-practice gap using a quality improvement approach. The Dean's Grand Rounds aims to embed the lived experience in clinical practice and use data to drive change. Methods We held five virtual Dean's Grand Rounds with this format from June 2022 to January 2024. The sessions included a variety of medical professionals, carers and expert patients presenting on the chosen theme, followed by a panel discussion. The sessions were then made available for on-demand viewing via the RCPsych website for those unable to attend the live session. Qualitative and quantitative feedback helped us improve the sessions iteratively. Results The sessions have enabled discussion of broader issues facing staff and patients, facilitating the exchange of ideas between professionals from divisions of the RCPsych from around the world. Participants globally attended these sessions, with over 1,000 registrations for the Grand Rounds on memory clinics and catatonia. The feedback for the sessions was overwhelmingly positive, with many participants praising the involvement of patients and carers and the opportunity to come together at the RCPsych level for learning. Many were attracted to the sessions because of the themes discussed, with 68.5% having excellent overall experience. Over 92% of the feedback participants thought the Grand Rounds had improved their professional practice. The majority of the feedback participants strongly agreed that lived experience is an important element in understanding the evidence-practice gap (4.4 on a Likert scale of 1, strongly disagree; 5, strongly agree) and that the Grand Rounds had enhanced their understanding of academic evidence and contextual data in the area (4.4, 4.39 respectively on a Likert scale of 1, strongly disagree; 5, strongly agree). The themes that stood out in the feedback were that participants liked the Grand Rounds format and were grateful to hear from patients, with suggestions to allocate more time for questions and answers. Learning from the feedback, we set up a resources page for each Grand Rounds to enable further learning. Conclusion In their revitalised format, these sessions are proving highly effective in bringing the worldwide RCPsych community together to improve patient care and deliver educational and informative interactive content available on demand.

The plants Buddleja asiatica and Buddleja paniculata are two closely related species of which the former has widespread uses in traditional medicine while the latter remains relatively unexplored. The present study focused on the analysis of phytoconstituents and the estimation of different biological activities of extracts and semi-purified fractions. The ethyl acetate stem fraction and methanolic extract of the leaf of B. asiatica displayed the highest phenolic contents of 390.98 ± 5.32 and 383.72 ± 7.31 mg GAE/g. TFC content was measured as 207.33 ± 0.34 and 138.58 ± 0.53 mg QE/g for ethyl acetate leaf and stem fraction of B. asiatica. The highest antioxidant activity was displayed by crude stem extract of B. paniculata with IC50 35.65±0.61 µg/mL in DPPH assay followed by ethyl acetate stem fraction of B. asiatica with IC50 36.17±0.92 µg/mL. The DCM leaf fraction and stem fraction of B. asiatica were found active against gram-negative and gram-positive bacteria. Both plants displayed mild to no toxicity against the brine shrimp nauplii. Median lethal concentration (LC50) ranged from 346.96 ± 25.39 in hexane fraction of leaf to 2719.32 ± 706.5 µg/mL in crude methanol extract of B. paniculata. The present study showed these medicinal plants are rich in an important class of organic compounds that play a significant role in the cure of bacterial infection which ultimately supports pharmaceutical research.

In the eastern coastal regions of Odisha, wilt caused by Fusarium oxysporum f. sp.capsici is an extremely damaging disease in chilli. This disease is very difficult to manage with chemical fungicides since it is soil-borne in nature. The natural rhizosphere soil of the chilli plant was used to isolate and test bacterial antagonists for their effectiveness and ability to promote plant growth. Out of the fifty-five isolates isolated from the rhizosphere of healthy chilli plants, five isolates, namely Iso 01, Iso 17, Iso 23, Iso 24, and Iso 32, showed their highly antagonistic activity against F. oxysporum f. sp. capsici under in vitro. In a dual culture, Iso 32 (73.3%) and Iso 24 (71.5%) caused the highest level of pathogen inhibition. In greenhouse trials, artificially inoculated chilli plants treated with Iso 32 (8.8%) and Iso 24 (10.2%) had decreased percent disease incidence (PDI), with percent disease reduction over control of 85.6% and 83.3%, respectively. Iso 32 and Iso 24 treated chilli seeds have shown higher seed vigor index of 973.7 and 948.8, respectively, as compared to untreated control 636.5. Furthermore, both the isolates significantly increased plant height as well as the fresh and dry weight of chilli plants under the rolled paper towel method. Morphological, biochemical, and molecular characterization identified Bacillus amyloliquefaciens (MH491049) as the key antagonist. This study demonstrates that rhizobacteria, specifically Iso 32 and Iso 24, can effectively protect chilli plants against Fusarium wilt while promoting overall plant development. These findings hold promise for sustainable and eco-friendly management of Fusarium wilt in chilli cultivation.

The present study investigates a Multi-Objective Transportation Problem within a fuzzy environment. The cost of transportation, supply, and demand data are assumed to be inaccurate due to market variations. As a result, the parameters are imprecise or fuzzy data. We offer a multi-objective, balanced transportation problem during this work, where all the parameters are fuzzy numbers. Following a mathematical formulation, fuzzy arithmetic will be used to divide the Fuzzy MOTP into three levels MOTP (lower, medium, upper). After reducing the problem to a crisp MOTP and applying a harmonic mean to each objective function, a suggested solution procedure is presented. Determining the optimal solutions for the FMOTP under unknown situations is, thus, the most important objective of this research.

BackgroundMultimodal lifestyle interventions, employing food as medicine, stand as the recommended first-line treatment for obesity. The Shared Medical Appointment (SMA) model, where a physician conducts educational sessions with a group of patients sharing a common diagnosis, offers an avenue for delivery of comprehensive obesity care within clinical settings. SMAs, however, are not without implementation challenges. We aim to detail our experience with three implementation models in launching a virtual integrative health SMA for weight management.MethodsEligible patients included individuals 18 years of age or older, having a body mass index (BMI) of 30 kg/m2 or 27 kg/m2 or greater with at least one weight related comorbidity. The Practical, Robust Implementation and Sustainability Model (PRISM), Plan, Do, Study, Act (PDSA), and the Framework for Reporting Adaptations and Modifications-Enhanced (FRAME) models were applied to guide the implementation of the Supervised Lifestyle Integrative Medicine (SLIM) program, a virtually delivered, lifestyle medicine focused SMA program, in a weight management clinic within a major health system. We describe how these models, along with attendance for the initial cohorts, were used for decision-making in the process of optimizing the program.Results172 patients completed the SLIM program over two years. Attendance was lowest for sessions held at 8:00 AM and 4:00 PM compared to sessions at 10:00 AM, 1:00 PM, and 3:00 PM, leading to only offering midday sessions (p = 0.032). Attendance data along with feedback from patients, facilitators, and administrative partners led to changes in the curriculum, session number and frequency, session reminder format, and intake visit number.ConclusionThe use of implementation and quality improvement models provided crucial insight for deployment and optimization of a virtual, lifestyle medicine focused SMA program for weight management within a large healthcare system.

IntroductionIndia has a high prevalence of Vitamin D insufficiency among women of childbearing age. In this study, we aimed to evaluate the potential relationship between Vitamin D deficiency and gestational diabetes mellitus (GDM) and low birth weight (LBW) of newborns in the “Maternal antecedents of adiposity and studying the transgenerational role of hyperglycaemia and insulin” (MAASTHI) birth cohort.MethodsA prospective cohort study involving 230 participants was conducted in public hospitals located in urban Bengaluru, India. Healthy pregnant women who visited these hospitals for antenatal care (ANC) and who were between 14 and 36 weeks of gestational age were recruited after obtaining their informed consent. An oral glucose tolerance test (OGTT) was administered between 24 and 36 weeks of pregnancy and blood samples were preserved at −80°C for Vitamin D analysis. Follow-up at birth included recording the child's birth weight.ResultsWe found that 178 (77.4%) of the study participants were vitamin D deficient, 44 (19.1%) were diagnosed with GDM, and 64 (27.8%) gave birth to LBW babies. Women in the lowest quartile of serum Vitamin D levels had three times higher odds of developing GDM than women in the higher quartiles [OR = 3.22 (95% CI: 1.03, 10.07), p = 0.04] after adjusting for age, parity, socioeconomic status, season, and adiposity. For every one-unit increase in Vitamin D levels, Homeostatic Model Assessment for Insulin Resistance (HOMA-IR) decreased by nearly 18%. Furthermore, causal mediation analysis showed that a decrease in one unit of Vitamin D is associated with a decrease of 0.015 units of fasting blood sugar (FBS) and 0.019 units of postprandial blood sugar (PPBS) as it flows through the mediator variable insulin resistance. Vitamin D-deficient women were twice at risk of giving birth to LBW babies (OR 2.04, 95% CI 0.99, 4.19, p = 0.05).DiscussionsLow levels of Vitamin D during pregnancy are associated with a greater risk of pregnant women developing GDM and giving birth to LBW babies in urban Bengaluru.

Microgrids are constituted of interconnected loads and dispersed energy resources. They increase the reliability of the power supply, decrease the cost of the per unit energy and support the green environment concept. Microgrids have gained importance due to their ability to deliver reliable energy and operate in both grid-connected and off-grid/island modes. Renewable energy sources such as photovoltaic (PV) and wind can be connected to the microgrid system to generate green energy. An electric vehicle charging station (EVCS) can be deployed in microgrids powered by renewable energy sources. An effective way to handle the rapidly rising load demand of electric vehicles (EVs) is to integrate EV recharge stations into microgrids. Due to the intermittent nature of solar PV and EV load's reliability, an evaluation of microgrids becomes challenging. Assessment of the components’ failure and their impact on the system reliability is proposed using different methods. Reliability Block Diagram (RBD), Markov Reliability Modeling (MRM) and Fault Tree Analysis (FTA) - Monte Carlo simulation are used for the Reliability Modeling of a community microgrid considering EVCS as a critical load. The mean time to failure (MTTF) is computed for the entire microgrid which includes PV, wind and fuel cell systems integrated into the grid with associated power electronic devices, transmission lines, transformers and point of common coupling (PCC). MATLAB software was used to program and study the reliability aspects of the microgrid. Using the FTA MCS technique and grid functions for 45 years with 20 % reliability, significant results were obtained in terms of a MTTF rate of 26.16 years.

Anorectal manometry is the most widely used technique for detecting abnormalities of sphincter function and recto-anal coordination. It enables the investigation of defaecatory difficulties, including anal incontinence and evacuatory difficulties, the assessment of patients with anal fistula and obstetric anal sphincter injury. Anorectal manometry is routinely used in combination with other tests, such as endoanal ultrasound or defaecatory imaging to understand the pathophysiology and aid treatment planning.There are various techniques and reporting protocols available but adopting standardised methods is essential for meaningful results and for comparing results between different units. This paper follows the workshop on anorectal manometry at the International Continence Society conference in Vienna in 2022. It aims to outline the various techniques and equipment available, with examples of high-resolution manometry cases and their interpretation.

The current study focuses on the combined experimental and theoretical FT-IR and FT-Raman spectra of 1-carboxy-4-ethoxybenzene (1C4EB), to obtain vibrational frequencies and optimal geometrical parameters by HF and DFT. For 1C4EB, the kind of intramolecular interactions and their stabilization were performed by Natural Bond Orbital analysis. The Nonlinear optical p properties and the conclusive evidence for ICT were also analyzed. The electronic energies and absorption spectra in different mediums were determined. Furthermore, Mulliken charges distribution, molecular electrostatic potential maps, the condensed Fukui function and thermodynamic parameters were calculated. The topological properties and IRI were analyzed with the Multiwfn program. In molecular docking, target proteins 4ULE and 2EEP were used to investigate sugar phosphatase and Prolyl aminopeptidase inhibitor properties. The least binding energy -6.7 kcal/mol is observed for the selected protein 4ULE. The molecular dynamics of the complex between the proteins 4ULE and 2EEP – ligand, binding free energies also calculated by the Poisson-Boltzmann surface area method.