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Background: The leather industry stands as one of India’s burgeoning sectors, boasting significant manpower. Workers in the finishing sector, particularly those involved in polishing, face heightened risks due to exposure to formaldehyde, aniline, and resins, which can lead to respiratory complications. Aims and Objectives: The aims and objectives of the study are to determine the small airway obstruction in asymptomatic finishing sector workers of a leather factory. Materials and Methods: The current study was a cross-sectional study conducted in a leather factory located in Chennai, Tamil Nadu. A random sample of 60 workers was drawn from different sectors of a leather factory. Basic demographic details, smoking history, nature of work, exposure to irritants along with duration, and respiratory symptoms were collected. All workers were subjected to spirometry as per ATS guidelines and the results were analyzed. Association between length (years) of exposure, respiratory symptoms, and spirometry impairment among the workers were analysed. Results: Out of 60 samples, 38 were exposed to irritants and 22 were not exposed to irritants. Out of 38, 25 had small airway disease, seven had large airway obstruction, one had a restrictive pattern, and five had normal spirometry. 42.1% of them were asymptomatic. Conclusion: Small airway obstruction is notably prevalent, especially among asymptomatic workers in the polishing sector. Prolonged exposure is associated with the development of large airway obstructive disease. This study underscores the importance of continuous monitoring for airway diseases in occupationally exposed workers, advocating for timely preventive and protective measures to curb disease progression.

Background and objective: Poststroke fatigue is widely overlooked due to a lack of awareness, definite cause, and cure. It is influenced by cultural variance in psychosocial factors and stroke epidemiology patterns across different countries. To date, the prevalence of poststroke fatigue has not been determined in the Indian setting. Hence our objective was to estimate the prevalence of fatigue among community-living late subacute and chronic stroke survivors. Additionally, we determined the association between poststroke fatigue and participants’ clinical and demographic data. Methods: A cross-sectional survey was conducted among first-time stroke survivors following discharge from two tertiary care hospitals. Medical records were screened and included if they met the criteria for age >18 years, able to follow a two-step command with no existing neurological or systemic conditions that would influence fatigue scores. Telephonic consent was obtained, and an interview was conducted to obtain the scores for fatigue, daytime sleepiness, depression, functional mobility, and disability. Results: A total of 323 participants (mean age: 60.3 ± 12.6 years, mean stroke duration: 8.6 ± 4.4 months) completed the interview. Poststroke fatigue was reported in 11.8 % (n = 38) of the population. Occupational status (χ2 = 5.73), functional mobility (χ2 = 5.84), and disability scores (χ2 = 8.00) were significantly associated (p

Aim and Background: A noticeable gap in the literature is observed in factors associated with the occurrence of respiratory illness among people working in garment industries. This study aims to investigate whether spirometry screening is warranted for individuals employed within the garment industry. Subjects and Methods: This is an observational cross-sectional pilot study conducted in asymptomatic workers of various sectors in a garment industry using spirometry to check for the presence of small airway obstruction. Duration of work along with the nature of work was taken into consideration to see if any correlation exists with the small airway obstruction. Results: A random sample of 40 asymptomatic individuals was drawn consisting of 16 males and 24 females with 47.5% of them working for around 5–10 years. Ninety-five percentage showed no evidence of small airway obstruction and only 5% showed small airway obstruction showing that no correlation exists between the duration of work and the development of small airway obstruction in the garment industry. Conclusion: There appears to be no significant link between the duration of employment in the garment industry and the development of small airway obstruction as compared to the textile/cotton industry, yet annual screening could be integrated into routine health assessments.

This study examines land use transformations and their impact on groundwater quality fluctuations in Chengalpattu district, Tamil Nadu. Chengalpattu is a rapidly urbanizing coastal region in South India. Landsat 5 and 9 satellite imagery were utilized to create land use land cover (LULC) maps for 2011 and 2021, with overall accuracies of 82.20% and 85.71%, respectively. The analysis showed significant growth in built-up and fallow lands due to urbanization, which has altered LULC. Groundwater Quality Index (GQI) was generated using seven groundwater quality parameters, adhering to World Health Organization (WHO) standards. The mean GQI showed slight fluctuations during pre- and post-monsoon periods, with a declining trend in pre-monsoon seasons. The study area predominantly experiences moderate groundwater quality, with fewer areas of low and high quality. The findings indicate that land use transformation significantly impacts groundwater quality, emphasizing the need for sustainable urban planning and water resource management in the region.

Progressive massive fibrosis (PMF) is a severe form and late-stage chronic pneumoconiosis defined by the clustering of silicotic nodules fused with connective tissue in silicosis and coal macules surrounded by fibrous tissue in coal workers’ pneumoconiosis. High levels of exposure to silica and coal mine dust are important risk factors for the development of PMF. A thorough occupational history and radiological investigation would help in the diagnosis and prevention of this condition, as there is no effective treatment. Hence, primary prevention is important as it has high mortality and morbidity. Newer health strategies, including periodical screening, evaluation, treatment, and awareness, are needed among workers who are exposed to these health hazards.

Abstract Connectome studies have shown how Alzheimer’s disease (AD) disrupts functional and structural connectivity among brain regions. But the molecular basis of such disruptions is less studied, with most genomic/transcriptomic studies performing within-brain-region analyses. To inspect how AD rewires the correlation structure among genes in different brain regions, we performed an Inter-brain-region Differential Correlation (Inter-DC) analysis of RNA-seq data from Mount Sinai Brain Bank on four brain regions (frontal pole, superior temporal gyrus, parahippocampal gyrus and inferior frontal gyrus, comprising 264 AD and 372 control human post-mortem samples). An Inter-DC network was assembled from all pairs of genes across two brain regions that gained (or lost) correlation strength in the AD group relative to controls at FDR 1%. The differentially correlated (DC) genes in this network complemented known differentially expressed genes in AD, and likely reflects cell-intrinsic changes since we adjusted for cell compositional effects. Each brain region used a distinctive set of DC genes when coupling with other regions, with parahippocampal gyrus showing the most rewiring, consistent with its known vulnerability to AD. The Inter-DC network revealed master dysregulation hubs in AD (at genes ZKSCAN1, SLC5A3, RCC1, IL17RB, PLK4, etc.), inter-region gene modules enriched for known AD pathways (synaptic signaling, endocytosis, etc.), and candidate signaling molecules that could mediate region-region communication. The Inter-DC network generated in this study is a valuable resource of gene pairs, pathways and signaling molecules whose inter-brain-region functional coupling is disrupted in AD, thereby offering a new perspective of AD etiology.

IntroductionAn immobile patient cannot be weighed on a stand-on weighing machine, i.e., a bathroom scale. They have to get weighed while lying, which is not easy. The main objective of this research is to design a medical apparatus that measures the patient's weight in a lying condition. To achieve this the apparatus is designed as a stretcher to carry the patient in and around the hospital.MethodsThe stretcher has four load cells to measure the patient's weight; it can bear a weight of 500 kg and has a self-weight of 20 kg. A Microcontroller unit (MCU) is embedded into the apparatus to weigh the patient lying on it. The stretcher comprises the top frame, middle frame, and base frame. The top frame can be detached and mounted back to the middle frame; this will help the medical personnel shift the patients from a medical bed. The middle frame is a plate structure where the four load cells are mounted at the corners of the lower plate. The upper plate functions as a pressure plate on the load cell. The base plate has four heavy-duty wheels that can bear the load. The middle frame and base frame, together, form a single structure, giving mobility to the structure. A control panel is employed with reset, tare, and on-off buttons to control the embedded platform. The LCD panel on the side of the apparatus shows the weight when the patient is placed on top of the apparatus.Results and discussionA prototype trolley equipped with a wireless data logging system was tested on 10 healthy participants. The device accurately measured weight within ±50 g across a scale range of 2–140 kg, with data captured every 30 s over a 5-min testing period. Wireless communication was successfully demonstrated over a 100-m range. The important add-on feature of this work is the apparatus is connected to the internet, transforming it into an IoT-based medical device.

The knowledge of the tracheobronchial tree is fundamental for a bronchoscopist, especially for diagnostic, therapeutic, and surgical options that are under consideration. Among all lobes, the right upper lobe (RUL) has a relatively high incidence of tumor and airspace diseases. Most bronchial anomalies are on the right side. Bifurcation is the most common anatomical variation. Quadrification has a very low prevalence. We report the case of a 54-year-old male who came with complaints of hemoptysis and showed a quadrivial type of bronchial division of RUL during a bronchoscopic examination after which three-dimensional computed tomography bronchography showed a supernumerary lateral or axillary segment bronchus. Awareness of this variant may explain otherwise confusing findings and its clinical implication is important for every pulmonologist.

Post-stroke fatigue (PSF) is a commonly overlooked symptom that impacts daily functioning and quality of life. It is caused by altered functional connectivity within the brain networks, which can potentially be influenced by neuromodulation. Multiple cortical regions have been targeted to reduce PSF, but the most efficient ones remain uncertain. Therefore, we aim to identify the most appropriate cortical stimulation site to reduce PSF. Twenty participants with PSF will be included in this cross-over trial. Each participant will receive one session of active anodal high definition- transcranial direct current stimulation (HD-tDCS) over three different cortical areas and one session of sham tDCS in a cross-over manner, with a two-week of washout period in between. Pre- and post- fatigue will be assessed using Fatigue Severity Scale and fatigability using electromyography by determining the time to task failure. Resting-state electroencephalography will be performed before and after each stimulation session to determine the functional connectivity of the cortical areas stimulated.

Parkinson’s Disease (PD) diagnosis with an explicit severity level assessment is essential for timely medical intervention. Gait analysis has been proven to assist in defining PD progression and Machine Learning algorithms aid in predicting the PD severity by learning intricate details from multidimensional gait datasets. Classifiers that best predict the severity level with better explainability of results are thus a requisite. This paper proposes an Information Set-based Decision Tree (IFS-DT), a classification algorithm that adopts a probabilistic-possibilistic approach for predicting the target variable. Information carried by each feature of the dataset is utilized rather than the possibility of its occurrence which reduces uncertainty in determining the outcome. PD gait dataset built from VGRF signals obtained from the PhysioNet database is utilized for analyzing the proposed work. The IFS-DT classifier performs better than conventional DTs and provides results comparable to that obtained by an ensemble of classifiers. Statistical analyses such as the Wilcoxon signed-rank test are conducted to validate the significance of the findings. Moreover, the importance of training a model with a balanced dataset with optimal sample size for obtaining reliable prediction results is also discussed.

Manufacturing has grown challenging because of the increased usage of harder materials, such as titanium alloys, in many industries, such as aerospace, automobiles, and marine. Conventional material removal procedures are not suitable for these tough materials due to their increased hardness and slow machinability. Wire Electrical discharge machining (WEDM) is a modern approach for material removal, particularly for harder materials, such as titanium alloys, nickel alloys, hard particle reinforced metal matrix composites, etc. The research design was performed by deeming the independent factors, such as duration of pulse and applied current. The removal rate of material, surface roughness of the machined region, dimensional deviation, and tolerance errors in form/orientation are considered performance metrics. Taguchi’s approach was engaged to assess the process variables, and the importance of the process factors was established using analysis of variance approach. The purpose of this research is to create an AI based decision making tool, which can be utilized to anticipate the various parameters that impact the WEDM material removal process. The discoveries of the present exploration allowing the manufacturers to make better-informed decisions with a developed model’s capability by demonstrating that the model’s predicted values were in close confirmation to the actual values.

Aluminium alloys are gaining popularity in a diversity of engineering applications because of their extraordinary features such as strength, resistance to oxidation, and so on. AA5052 (Al-Mg series) is generally used in antirust uses, particularly in desalination related activities, due to its better resistance to corrosion in marine applications at temperature ranges up to 125°C, lower cost, better heat-carrying capacity, and nontoxicity of its corrosion components. Drilling is one of the most commonly adopted material removal processes that is adopted in numerous engineering uses. Taguchi’s technique is engaged to arrange and examine the tests, by treating the drilling diameter and speed and as independent process factors. The studies were carried out using an L27 orthogonal array. Material removal rate (MRR), surface roughness (SR), and form/orientation error are deemed as output characteristics. Taguchi’s analysis was engaged to discover the best process factors. ANOVA is used to examine the influence of process variables. Suitable application of artificial intelligence tools for making effective decision assists the manufacturer in accomplishing the benefits in numerous engineering domains. To obtain the maximum material removal and minimum roughness, circularity (circ), and perpendicularity errors (perp), the process variables have been optimized with the help of grey-ANFIS-amalgamated with Jaya algorithm. The multiperformance index was developed using grey theory. Statistical error analysis is used to estimate the performance of the established optimization model. Based on the investigative outcomes, the best-suited process variable combinations will be used to provide improved and enhanced multiperformance characteristics.

Background Activity monitoring is a necessary technique to ensure stroke survivors’ activity levels in the hospital are within optimal levels as this is important for enhanced motor recovery. However, this could be time-consuming for healthcare professionals like physiotherapists. Activity monitoring by caregivers could be an alternate option. Therefore, our aim was to compare the activity monitoring of stroke survivors by caregivers and physiotherapists during early phase in a hospital setting. Methods An observation study was carried out in the neuroscience ward in a tertiary care hospital among 17 stroke survivors. Physiotherapist and caregivers were instructed to use an activity log chart that was developed during previous research conducted by the same authors for observing the activities performed by the patients every 15 minutes from 8 AM to 5 PM across one day. Data collected were analysed using Stata 15. Kappa statistics were carried out to determine the agreement of the observations between the two raters. Results A total of 10 male and seven female caregivers of stroke survivors with a mean age of 40.11 ± 9.2 years and a trained physiotherapist participated in the study. A total of 272 observations of caregivers were in agreement with that of the physiotherapist. Inter-rater Kappa statistics showed 60% agreement between the physiotherapist and the caregivers (p

With the evolution of multicellularity, communication among cells in different tissues and organs became pivotal to life. Molecular basis of such communication has long been studied, but genome-wide screens for genes and other biomolecules mediating tissue-tissue signaling are lacking. To systematically identify inter-tissue mediators, we present a novel computational approach MultiCens (Multilayer/Multi-tissue network Centrality measures). Unlike single-layer network methods, MultiCens can distinguish within- vs. across-layer connectivity to quantify the "influence" of any gene in a tissue on a query set of genes of interest in another tissue. MultiCens enjoys theoretical guarantees on convergence and decomposability, and performs well on synthetic benchmarks. On human multi-tissue datasets, MultiCens predicts known and novel genes linked to hormones. MultiCens further reveals shifts in gene network architecture among four brain regions in Alzheimer's disease. MultiCens-prioritized hypotheses from these two diverse applications, and potential future ones like "Multi-tissue-expanded Gene Ontology" analysis, can enable whole-body yet molecular-level systems investigations in humans.

Background: Action observation training (AOT) is used for lower limb (LL) stroke rehabilitation in subacute and chronic stages, but concise information regarding the types of activities to be used and the feasibility of administration in the acute stroke population is unknown. The aim of this study was to develop and validate videos of appropriate activities for LL AOT and test administrative feasibility in acute stroke. Method: A video inventory of LL activities was created after a literature survey and expert scrutiny. Five stroke rehabilitation experts validated the videos per domains of relevance, comprehension, clarity, camera position and brightness. LL AOT was then tested on ten individuals with acute stroke for uncovering barriers for clinical use in a feasibility study. Participants watched the activities and attempted imitation of the same. Determination of administrative feasibility was undertaken via participant interviews. Results: Suitable LL activities for stroke rehabilitation were identified. Content validation of videos led to improvements in selected activities and video quality. Expert scrutiny led to further video processing to include different perspectives of view and speeds of projected movements. Barriers identified included inability to imitate actions shown in videos and increased distractibility for some participants. Conclusion: A video catalogue of LL activities was developed and validated. AOT was deemed safe and feasible for acute stroke rehabilitation and may be used in future research and clinical practice.

Titanium alloys have found widespread use in aviation, automotive, and marine applications, which makes their implementation in mass production more challenging. Conventional methods of removing these alloy materials are unsuitable because of the high wear rate of cutting and slower rate of processing. The complexities of these materials have prompted the creation of cutting-edge machining methods. Wire Electrical Discharge Machining (WEDM) is a technique that has the potential to be useful for the removal of materials that are harder and electrically conductive. In order to create intricate designs, this method is frequently employed. The input factors, including pulse duration (on/off) and peak current, were taken into account during the experimental design process. The rate of material removal, surface roughness, dimensional deviation, and GD&T errors were opted for as performance indicators. The approach proposed by Taguchi was selected for the investigation of the process factors, and an Analysis of Variance was selected to find out the relative momentousness of each factor. From the analysis it is perceived that the applied current is the predominant factor that influences the chosen output characteristics. The aspiration of this article is to evolve a decision-making model based on a hybrid learning method which can be adopted to predict the selected output measures that affect the WEDM process. According to the findings, the value of the ANFIS-GRG, which was predicted to be 0.7777, was in fact closer to that value than any other value. The proposed model has the ability to help make a variety of different production processes more efficient. The analysis showed that the model’s functionality was enhanced, which helps producers make well-informed decisions.

Recently, research into Wireless Body-Area Sensor Networks (WBASN) or Wireless Body-Area Networks (WBAN) has gained much importance in medical applications, and now plays a significant role in patient monitoring. Among the various operations, routing is still recognized as a resource-intensive activity. As a result, designing an energy-efficient routing system for WBAN is critical. The existing routing algorithms focus more on energy efficiency than security. However, security attacks will lead to more energy consumption, which will reduce overall network performance. To handle the issues of reliability, energy efficiency, and security in WBAN, a new cluster-based secure routing protocol called the Secure Optimal Path-Routing (SOPR) protocol has been proposed in this paper. This proposed algorithm provides security by identifying and avoiding black-hole attacks on one side, and by sending data packets in encrypted form on the other side to strengthen communication security in WBANs. The main advantages of implementing the proposed protocol include improved overall network performance by increasing the packet-delivery ratio and reducing attack-detection overheads, detection time, energy consumption, and delay.

Background: The HIV sentinel surveillance (HSS) serves to estimate the levels and trend of HIV prevalence among high-risk, bridge, and general population and monitors HIV management at national, state, and district levels. Data from HSS are valuable in understanding the risk factors associated with HIV transmission with particular demographic characteristics. Objectives: The objective was to analyze the sociodemographic profile of the pregnant mothers attending the antenatal care (ANC) clinics in Karnataka, in order to understand the dynamics of HIV within the general population in Karnataka. Materials and Methods: Study design: this was a cross-sectional study conducted using consecutive sampling method. Study setting: the surveillance was conducted at select antenatal clinics, in Karnataka, India, between January and March, in the years 2013, 2015, and 2017. Methodology: in total, 74,278 eligible pregnant women aged between 15 and 49 years, attending the sentinel sites for the first time during the surveillance period, were included in the study. Information on their sociodemographic characteristics and blood samples was collected. Results: HIV prevalence among the ANC clinic attendees has significantly declined, reaching a recent stabilization. The risk factors significantly associated with HIV among pregnant women were age, education, occupation, and marital status. HIV is highly concentrated in the northern and southern districts of Karnataka. Conclusion: Despite the declining trends of HIV prevalence in Karnataka, the epidemic is heterogeneous and concentrated within the state, calling for decentralized region-specific interventions.

Integrating cloud computing with wireless sensor networks creates a sensor cloud (WSN). Some real-time applications, such as agricultural irrigation control systems, use a sensor cloud. The sensor battery life in sensor clouds is constrained. The data center’s computers consume a lot of energy to offer storage in the cloud. The emerging sensor cloud technology-enabled virtualization. Using a virtual environment has many advantages. However, different resource requirements and task execution cause substantial performance and parameter optimization issues in cloud computing. In this study, we proposed the hybrid electro search with ant colony optimization (HES-ACO) technique to enhance the behavior of task scheduling, for those considering parameters such as total execution time, cost of the execution, makespan time, the cloud data center energy consumption like throughput, response time, resource utilization task rejection ratio, and deadline constraint of the multicloud. Electro search and the ant colony optimization algorithm are combined in the proposed method. Compared to HESGA, HPSOGA, AC-PSO, and PSO-COGENT algorithms, the created HES-ACO algorithm was simulated at CloudSim and found to optimize all parameters.

Background and Objective: Periodic tracking of the trends and the levels of HIV prevalence at regional and district levels helps to strengthen a state’s HIV/AIDS response. HIV prevalence among pregnant women is crucial for the HIV prevalence estimation of the general population. Karnataka is one of the high HIV prevalence states in India. Probing regional and district levels and trends of HIV prevalence provides critical insights into district-level epidemic patterns. This paper analyzes the region- and district-wise levels and trends of HIV prevalence among pregnant women attending the antenatal clinics (ANC) from 2003 to 2019 in Karnataka, South India. Methods: HIV prevalence data collected from pregnant women in Karnataka during HIV Sentinel Surveillance (HSS) between 2003 and 2019 was used for trend analysis. The consistent sites were grouped into four zones (Bangalore, Belgaum, Gulbarga and Mysore regions), totaling 60 sites, including 30 urban and 30 rural sites. Regional and district-level HIV prevalence was calculated; trend analysis using Chi-square trend test and spatial analysis using QGIS software was done. For the last three HSS rounds, HIV prevalence based on sociodemographic variables was calculated to understand the factors contributing to HIV positivity in each region. Results: In total, 254,563 pregnant women were recruited. HIV prevalence in Karnataka was 0.22 (OR: 0.15 95% CI: 0.16 - 0.28) in 2019. The prevalence was 0.24, 0.32, 0.17 and 0.14 in Bangalore, Belgaum, Gulbarga, and Mysore regions, respectively. HIV prevalence had significantly (P< 0.05) declined in 26 districts. Conclusion and Global Health Implications: HIV prevalence among pregnant women was comparatively higher in Bangalore and Belgaum regions. Analysis of contextual factors associated with the transmission risk and evidence-based targeted interventions will strengthen HIV management in Karnataka. Regionalized, disaggregated, sub-national analyses will help identify emerging pockets of infections, concentrated epidemic zones and contextual factors driving the disease transmission. Copyright © 2021. Arumugam et al. Published by Global Health and Education Projects, Inc. This is an open-access article distributed under the terms of the Creative Commons Attribution License CC BY 4.0.

Due to the vast development in the infrastructure section, the production of cement-based concrete is a major driving source for the increased global warming and extensive deployment of natural resources such as river sand. To reduce and mitigate these adverse effects, industrial by-products can be effectively used either in partial or complete levels to replace conventional materials such as cement, river sand, etc without compromising the strength and durability characteristics of concrete. This research work focuses on the experimental investigation of the thermal properties, strength, durability and microstructure analysis of optimized green concrete with pond ash and ground granulated blast-furnace slag (GGBS). The novelty of the proposed work lies in the investigation of the thermal and durability characteristics of sustainable green concrete with GGBS and pond ash as a partial replacement for cement and fine aggregate respectively. An optimum mix ratio obtained from the material characterization of 16 trail mixes was tested for mechanical properties, durability and thermal characterization. Moreover, the microstructure analysis of the optimized mix was performed using Scanning Electron Microscope (SEM) to overview the chemical constituents, bonding of molecules at the interfacial transition zone (ITZ), the effect of elevated temperature, etc Results from the trail mixes revealed that the replacement of 30% GGBS and 20% pond ash increased the compressive strength by 8% at 28 days of curing when compared to the control mix. In addition, a detailed multilinear regression analysis was performed and a new equation was proposed to determine the compressive strength of concrete with GGBS and pond ash. The predictions obtained from the proposed equation showed a good match with the benchmark experiments.

Background: HIV/AIDS is a global public health issue and its transmission in a defined geographic region is influenced by the interplay of sociodemographic and behavioral factors. Better understanding of sociodemographic characteristics of HIV-positive individuals is required to prevent the spread of HIV among the general population. Objectives: The objective of the study was to find the association between HIV prevalence and sociodemographic characteristics of pregnant women aged 15–49 years attending the antenatal clinics (ANCs) in six Southern states of India. Methods: The data from the latest round of HIV sentinel surveillance, a cross-sectional study, conducted during January–March 2017 among ANC attendees were considered for this analysis. Blood samples along with other relevant information were collected from 98,634 pregnant women from 248 sites across the states. The association between HIV prevalence and sociodemographic variables was examined using multivariable logistic regression. Results: The highest HIV prevalence was reported in Karnataka (0.38%) and Andhra Pradesh (0.38%), followed by Telangana (0.33%), Odisha (0.28%), Tamil Nadu (0.27%), and Kerala (0.05%). In all states, the prevalence was highest among illiterate pregnant women exception being Kerala, wherein the prevalence was highest in pregnant women with schooling up to primary education. A significant association was found between HIV prevalence and spouse occupation in Karnataka and Odisha and spouse migration in Andhra Pradesh and Karnataka. Conclusions: Need for improvising the interventions for the young, illiterates, having a migrant spouse, and spouse occupation as truckers/hotel staff is recommended to the stakeholders involved in HIV management of the six southern states of India.

Introduction After a stroke, 55% of survivors do not regain the ability to completely use their arm in daily life functioning. Currently, evidence-based guidelines recommend functional training for improving the affected hand after stroke. However, promoting an optimal quantity and quality of functional training is influenced by personal and environmental contextual factors. Studies that comprehensively target multiple factors regulating arm use are limited. This study compares the effects of functional training to multifactorial context-enhancing functional training program for improving functional arm use and recovery after stroke.Methods and analysis This is a protocol for an observer-blinded, two parallel groups, randomised controlled trial. A total of 126 community-dwelling subacute and chronic stroke survivors will be included in the study. A tailor-made multifactorial context-enhancing intervention-incorporating education, environmental enrichment and behaviour change techniques to reinforce functional training will be provided to the experimental group. The functional training group will be provided with functional exercises. The intervention will be delivered for 2 months. The primary outcomes of functional arm use and recovery will be measured using Motor Activity Log, Goal Attainment Scale and Rating of Everyday Arm-use in the Community and Home scale. The secondary outcomes of arm motor impairment and function will be measured using Fugl-Meyer upper limb score, Action Research Arm Test, ABILHAND questionnaire and Stroke Impact Scale. These will be measured at three points in time: before, after 2 months and after 1-month follow-up. The outcome measures will be analysed using one-way analysis of variance and regression analysis will be performed to identify factors limiting optimal task practice.Ethics and dissemination The study has been approved by the Institutional Ethics Committee of Kasturba Hospital, Manipal, India. Participants will sign a written informed consent prior to participation. The results will be published on completion of the trial and communicated to community-dwelling stroke survivors.Trial registration number CTRI/2017/10/010108

The initial goal of this study was to investigate alterations in adenosine A2A receptor (A2AR) density or function in a rat model of Huntington disease (HD) with reported insensitivity to an A2AR antagonist. Unsuspected negative results led to the hypothesis of a low striatal adenosine tone and to the search for the mechanisms involved. Extracellular striatal concentrations of adenosine were measured with in vivo microdialysis in two rodent models of early neuropathological stages of HD disease, the Tg51 rat and the zQ175 knock-in mouse. In view of the crucial role of the equilibrative nucleoside transporter (ENT1) in determining extracellular content of adenosine, the binding properties of the ENT1 inhibitor [3H]-S-(4-Nitrobenzyl)-6-thioinosine were evaluated in zQ175 mice and the differential expression and differential coexpression patterns of the ENT1 gene (SLC29A1) were analyzed in a large human cohort of HD disease and controls. Extracellular striatal levels of adenosine were significantly lower in both animal models as compared with control littermates and striatal ENT1 binding sites were significantly upregulated in zQ175 mice. ENT1 transcript was significantly upregulated in HD disease patients at an early neuropathological severity stage, but not those with a higher severity stage, relative to non-demented controls. ENT1 transcript was differentially coexpressed (gained correlations) with several other genes in HD disease subjects compared to the control group. The present study demonstrates that ENT1 and adenosine constitute biomarkers of the initial stages of neurodegeneration in HD disease and also predicts that ENT1 could constitute a new therapeutic target to delay the progression of the disease.

Abstract Using expression profiles from postmortem prefrontal cortex samples of 624 dementia patients and non‐demented controls, we investigated global disruptions in the co‐regulation of genes in two neurodegenerative diseases, late‐onset Alzheimer's disease (AD) and Huntington's disease (HD). We identified networks of differentially co‐expressed (DC) gene pairs that either gained or lost correlation in disease cases relative to the control group, with the former dominant for both AD and HD and both patterns replicating in independent human cohorts of AD and aging. When aligning networks of DC patterns and physical interactions, we identified a 242‐gene subnetwork enriched for independent AD/HD signatures. This subnetwork revealed a surprising dichotomy of gained/lost correlations among two inter‐connected processes, chromatin organization and neural differentiation, and included DNA methyltransferases, DNMT1 and DNMT3A, of which we predicted the former but not latter as a key regulator. To validate the inter‐connection of these two processes and our key regulator prediction, we generated two brain‐specific knockout (KO) mice and show that Dnmt1 KO signature significantly overlaps with the subnetwork (P = 3.1 × 10−12), while Dnmt3a KO signature does not (P = 0.017).

Quantifying heterogeneity in gene expression among single cells can reveal information inaccessible to cell-population averaged measurements. However, the expression level of many genes in single cells fall below the detection limit of even the most sensitive technologies currently available. One proposed approach to overcome this challenge is to measure random pools of k cells (e.g., 10) to increase sensitivity, followed by computational "deconvolution" of cellular heterogeneity parameters (CHPs), such as the biological variance of single-cell expression levels. Existing approaches infer CHPs using either single-cell or k-cell data alone, and typically within a single population of cells. However, integrating both single- and k-cell data may reap additional benefits, and quantifying differences in CHPs across cell populations or conditions could reveal novel biological information. Here we present a Bayesian approach that can utilize single-cell, k-cell, or both simultaneously to infer CHPs within a single condition or their differences across two conditions. Using simulated as well as experimentally generated single- and k-cell data, we found situations where each data type would offer advantages, but using both together can improve precision and better reconcile CHP information contained in single- and k-cell data. We illustrate the utility of our approach by applying it to jointly generated single- and k-cell data to reveal CHP differences in several key inflammatory genes between resting and inflammatory cytokine-activated human macrophages, delineating differences in the distribution of 'ON' versus 'OFF' cells and in continuous variation of expression level among cells. Our approach thus offers a practical and robust framework to assess and compare cellular heterogeneity within and across biological conditions using modern multiplexed technologies.

Many genome-wide datasets are routinely generated to study different aspects of biological systems, but integrating them to obtain a coherent view of the underlying biology remains a challenge. We propose simultaneous clustering of multiple networks as a framework to integrate large-scale datasets on the interactions among and activities of cellular components. Specifically, we develop an algorithm JointCluster that finds sets of genes that cluster well in multiple networks of interest, such as coexpression networks summarizing correlations among the expression profiles of genes and physical networks describing protein-protein and protein-DNA interactions among genes or gene-products. Our algorithm provides an efficient solution to a well-defined problem of jointly clustering networks, using techniques that permit certain theoretical guarantees on the quality of the detected clustering relative to the optimal clustering. These guarantees coupled with an effective scaling heuristic and the flexibility to handle multiple heterogeneous networks make our method JointCluster an advance over earlier approaches. Simulation results showed JointCluster to be more robust than alternate methods in recovering clusters implanted in networks with high false positive rates. In systematic evaluation of JointCluster and some earlier approaches for combined analysis of the yeast physical network and two gene expression datasets under glucose and ethanol growth conditions, JointCluster discovers clusters that are more consistently enriched for various reference classes capturing different aspects of yeast biology or yield better coverage of the analysed genes. These robust clusters, which are supported across multiple genomic datasets and diverse reference classes, agree with known biology of yeast under these growth conditions, elucidate the genetic control of coordinated transcription, and enable functional predictions for a number of uncharacterized genes.

Co-expression networks are routinely used to study human diseases like obesity and diabetes. Systematic comparison of these networks between species has the potential to elucidate common mechanisms that are conserved between human and rodent species, as well as those that are species-specific characterizing evolutionary plasticity. We developed a semi-parametric meta-analysis approach for combining gene-gene co-expression relationships across expression profile datasets from multiple species. The simulation results showed that the semi-parametric method is robust against noise. When applied to human, mouse, and rat liver co-expression networks, our method out-performed existing methods in identifying gene pairs with coherent biological functions. We identified a network conserved across species that highlighted cell-cell signaling, cell-adhesion and sterol biosynthesis as main biological processes represented in genome-wide association study candidate gene sets for blood lipid levels. We further developed a heterogeneity statistic to test for network differences among multiple datasets, and demonstrated that genes with species-specific interactions tend to be under positive selection throughout evolution. Finally, we identified a human-specific sub-network regulated by RXRG, which has been validated to play a different role in hyperlipidemia and Type 2 diabetes between human and mouse. Taken together, our approach represents a novel step forward in integrating gene co-expression networks from multiple large scale datasets to leverage not only common information but also differences that are dataset-specific.

A sustainable biorefining and bioprocessing strategy was developed to produce edible-ulvan films and non-edible polyhydroxybutyrate films. The preparation of edible-ulvan films by crosslinking and plasticisation of ulvan with citric acid and xylitol was investigated using Fourier transform infrared (FTIR) spectroscopy and differential scanning calorimetry (DSC) analysis. The edible ulvan film was tested for its gut-friendliness using Lactobacillus and Bifidobacterium spp. (yoghurt) and was shown to improve these gut-friendly microbiome's growth and simultaneously retarding the activity of pathogens like Escherchia coli and Staphylococcus aureus. Green macroalgal biomass refused after the extraction of ulvan was biologically processed by dark fermentation to produce a maximum of 3.48 (± 0.14) g/L of volatile fatty acids (VFAs). Aerobic processing of these VFAs using Cupriavidus necator cells produced 1.59 (± 0.12) g/L of biomass with 18.2 wt% polyhydroxybutyrate. The present study demonstrated the possibility of producing edible and non-edible packaging films using green macroalgal biomass as the sustainable feedstock., (© 2023. The Author(s).)

Food spoilage is an ongoing global issue that contributes to rising carbon dioxide emissions and increased demand for food processing. This work developed anti-bacterial coatings utilising inkjet printing of silver nano-inks onto food-grade polymer packaging, with the potential to enhance food safety and reduce food spoilage. Silver nano-inks were synthesised via laser ablation synthesis in solution (LaSiS) and ultrasound pyrolysis (USP). The silver nanoparticles (AgNPs) produced using LaSiS and USP were characterised using transmission electron microscopy (TEM), Fourier transform infrared (FTIR) spectroscopy, UV-Vis spectrophotometry and dynamic light scattering (DLS) analysis. The laser ablation technique, operated under recirculation mode, produced nanoparticles with a small size distribution with an average diameter ranging from 7-30 nm. Silver nano-ink was synthesised by blending isopropanol with nanoparticles dispersed in deionised water. The silver nano-inks were printed on plasma-cleaned cyclo-olefin polymer. Irrespective of the production methods, all silver nanoparticles exhibited strong antibacterial activity against E. coli with a zone of inhibition exceeding 6 mm. Furthermore, silver nano-inks printed cyclo-olefin polymer reduced the bacterial cell population from 1235 (±45) × 10 6 cell/mL to 960 (±110) × 10 6 cell/mL. The bactericidal performance of silver-coated polymer was comparable to that of the penicillin-coated polymer, wherein a reduction in bacterial population from 1235 (±45) × 10 6 cell/mL to 830 (±70) × 10 6 cell/mL was observed. Finally, the ecotoxicity of the silver nano-ink printed cyclo-olefin polymer was tested with daphniids, a species of water flea, to simulate the release of coated packaging into a freshwater environment.

The rapid depletion of crude-oil resource which sustains a conventional petroleum refinery together with its environmental impact has led to the search for more sustainable alternatives. In this context, biorefinery serves to fulfil the aim by utilizing waste resources. Hence, this study focused on techno-economic assessment of PHB production at large scale from waste carob pods in a closed-loop biorefinery setup. Firstly, the use of pure sugars in SC1 was shifted to use of carob pods as feedstock in SC2, upgradation of stirred tank bioreactor with novel annular gap bioreactor in SC3 and replacing the conventional centrifugation process with the upcoming ceramic membrane separation process in SC4. An Aspen plus™ flowsheet was developed by including the aforementioned novel strategies for PHB production. The effectiveness of PHB production under various scenarios was evaluated based on its pay-out period and turnover accumulated at the end of 7th year of a PHB plant operation. Instead of pure sugars as the feedstock (SC1), carob pod extract (SC2) reduced the pay-out period from 12.6 to 6.8 years. Likewise, switching onto ABR from the conventional STBR further decreased the pay-out period to 4.8 years. Finally, the use of ceramic membranes (SC4) instead of centrifugation resulted in a similar pay-out period of 4.8 years with increased turnover of about 1.4 billion USD. Thus, the use of carob pods along with an improved PHB titre in ABR and incorporation of affordable ceramic membrane technology for PHB rich biomass separation resulted in a highly cost-effective PHB production strategy., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

An annular bioreactor (ABR) with wide gap was used for PHB production from Ralstonia eutropha. Hydrodynamic studies demonstrated the uniform distribution of fluid in the ABR due to the Taylor-Couette flow. Thereafter, the ABR was operated at different agitation and sparging rates to study its effect on R. eutropha growth and PHB production. The ABR operated at 500 rpm with air sparge rate of 0.8 vvm yielded a maximum PHB concentration of 14.89 g/L, which was nearly 1.4 times that obtained using a conventional stirred-tank bioreactor (STBR). Furthermore, performances of the bioreactors were compared by operating the reactors under fed-batch mode. At the end of 90 h of operation, the ABR resulted in a very high PHB production of 70.8 g/L. But STBR resulted in a low PHB concentration of 44.2 g/L. The superior performance was due to enhanced oxygen and nutrient mass transfer in the ABR., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Polyhydroxybutyrate (PHB) is a natural polyester of microbial origin and is an excellent substitute for petroleum-based food packaging materials. However, moderate mechanical, thermal and barrier properties limit utilization of PHB for commercial food packaging applications. In order to overcome these drawbacks, the present study evaluated the solution casting method for the preparation of PHB nanocomposite by incorporating various concentration (0-1.3 wt%) of graphene nanoplatelets (Gr-NPs). The prepared nanocomposites were tested for their morphology, mechanical, thermal, barrier, cytotoxicity and biodegradable properties. A Gr-NPs concentration of 0.7 wt% was found to be optimum without any agglomeration. In comparison with pristine PHB, the PHB/Gr-NPs nanocomposite showed a higher melting point (by 10 °C), thermal stability (by 10 °C), tensile strength (by 2 times) along with 3 and 2 times reduction in oxygen and water vapour permeability, respectively. The penetration of UV and visible light was greatly reduced with the addition of Gr-NPs. Furthermore, cytotoxic effect of the prepared nanocomposite was found to be statistically insignificant in comparison with the pristine PHB. A four-fold increase in the shelf life was demonstrated by a simulation study conducted using moisture and oxygen-sensitive food items (potato chips and milk product)., Competing Interests: Declaration of competing interest The authors declare that they have no competing interest., (Copyright © 2020 Elsevier B.V. All rights reserved.)

A novel closed-loop biorefinery model using carob pods as the feed material was developed for PHB production. The carob pods were delignified, and as the second step, sugars present in the delignified carob pods were extracted using water. Ralstonia eutropha and Bacillus megaterium were cultivated on the carob pod extract and its performance was evaluated using Taguchi experimental design. R. eutropha outperformed the B. megaterium in terms of its capability to grow at a maximum initial sugar concentration of 40 g L -1 with a maximum PHB production of 12.2 g L -1 . Finally, the concentrated lignin from the first step was diluted with different proportion of chloroform to extract PHB from the bacterial biomass. The PHB yield and purity obtained were more than 90% respectively using either R. eutropha or B. megaterium. Properties of the PHB produced in this study were examined to establish its application potential., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

This study evaluated a novel shortcut nitrogen removal method using a mixed consortium of microalgae, enriched ammonia oxidizing bacteria (AOB) and methanol utilizing denitrifier (MUD) in a photo-sequencing batch reactor (PSBR) for treating ammonium rich wastewater (ARWW). Alternating light and dark periods were followed to obtain complete biological nitrogen removal (BNR) without any external aeration and with the addition of methanol as the sole carbon source, respectively. The results showed that influent NH 4 + was oxidized to NO 2 - by AOB during the light periods at a rate of 8.09 mg NH 4 + -N L -1 h -1 . Subsequently, NO 2 - was completely reduced during the dark period due to the action of MUD in presence of methanol. The high activities of ammonia monooxygenase (AMO) and nitrite reductase (NIR) enzymes revealed the strong role of AOB and MUD for achieving shortcut nitrogen removal from the wastewater. The reduced activities of nitrate reductase (NR) and nitrite oxidoreductase (NOR) at a high concentration of DO, NH 4 + and NO 2 - in the system further confirmed the nitrogen removal pathway involved in the process. The biomass produced from these experiments showed good settling properties with a maximum sedimentation rate of 0.7-1.8 m h -1 , a maximum sludge volume index (SVI) of 193 ml g -1 - 256 ml g -1 and floc size of 0.2-1.2 mm. In order to describe the growth and interaction among the algae, AOB and MUD for nitrogen removal in the system, the experimental results were fitted to four metabolic models, which revealed best fit of the experimental data due to the models based on algae-AOB and algae-AOB-MUD activities than with the other two models., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

This study evaluated an enhancement of simultaneous polycyclic aromatic hydrocarbon (PAH) biodegradation and lipid accumulation by Rhodococcus opacus using biochar derived cheaply from biomass gasification effluent. The chemical, physical, morphological, thermal, and magnetic properties of the cheaply derived biochar were initially characterized employing different techniques, which indicated that the material is easy to separate, recover, and reuse for further application. Batch experiments were carried out to study biochar-aided PAH biodegradation by R. opacus clearly demonstrating its positive effect on PAH biodegradation and lipid accumulation by the bacterium utilizing the synthetic media containing 2-, 3- or 4-ring PAH compounds, at an initial concentration in the range 50-200 mg L -1 , along with 10% (w/v) inoculum. An enhancement in PAH biodegradation from 79.6 to 92.3%, 76.1 to 90.5%, 74.1 to 88.2%, and 71.6 to 82.3% for naphthalene, anthracene, phenanthrene, and fluoranthene, respectively, were attained with a corresponding lipid accumulation of 68.1%, 74.2%, 72.4%, and 63% (w/w) of cell dry weight (CDW). From contact angle measurements carried out in the study, enhancement in PAH biodegradation and lipid accumulation due to the biochar was attributed to an improved bioavailability of PAH to the degrading bacterium.

Discharge of wastewater from electroplating and leather industries is a major concern for the environment due to the presence of toxic Cr 6+ and other ions, such as sulfate, nitrate, phosphate, etc. This study evaluated the potential of Tradescantia pallida, a plant species known for its Cr bioaccumulation, for the simultaneous removal of Cr 6+ , SO 4 2- , NO 3 - , and PO 4 3- . The effect of different co-ions on Cr 6+ removal by T. pallida was examined following the Plackett-Burman design of experiments carried out under batch hydroponics conditions. The results revealed a maximum removal of 84% Cr 6+ , 87% SO 4 2- , 94% NO 3 - and 100% PO 4 3- without any phytotoxic effect on the plant for an initial Cr 6+ concentration in the range 5-20 mg L -1 . SO 4 2- and NO 3 - enhanced Cr uptake at a high initial Cr concentration (20 mg L -1 ), whereas PO 4 3- did not affect Cr uptake both at high and low initial Cr concentrations. The Cr 6+ removal kinetics in the presence of different ions was well described by the pseudo-second-order kinetic model which revealed that both biosorption and bioaccumulation of the metal played an important role in Cr 6+ removal. Increase in the total carbohydrate and protein content of the plant following Cr 6+ and co-ions exposure indicated a good tolerance of the plant toward Cr 6+ toxicity. Furthermore, enhancement in the lipid peroxidation and catalase activity in T. pallida upon Cr 6+ exposure revealed a maximum stress-induced condition in the plant. Overall, this study demonstrated a very good potential of the plant T. pallida for Cr 6+ removal from wastewater even in the presence of co-ions.

Sulfate reduction by carbon monoxide (CO) utilizing anaerobic biomass from a large scale upflow anaerobic sludge blanket reactor was studied. Anaerobic mixed microbial consortia from five different sources were initially examined for their biological CO conversion potential. Among the different biomass, the biomass from an upflow anaerobic sludge blanket reactor treating domestic wastewater, located in Kavoor, Karnataka, India, showed a maximum CO conversion efficiency. The effect of three main culture parameters, i.e. inoculum volume, initial CO concentration and temperature on simultaneous CO conversion and sulfate reduction was assessed employing the Taguchi experimental design technique. A maximum CO conversion of 85.62% and a maximum sulfate reduction of 50.65% were achieved. Furthermore, the experimental data was fitted to substrate inhibition models reported in the literature. Among the different models, Monods and Haldane kinetic models were found most suitable to describe the kinetics of biomass growth and CO removal by the anaerobic biomass., (Copyright © 2015 Elsevier Ltd. All rights reserved.)

In this study, Nostoc muscorum, a native cyanobacterial species isolated from a coal mining site, was employed to remove Cu(II), Zn(II), Pb(II) and Cd(II) from aqueous solution containing these metals in the mixture. In this multicomponent study, carried out as per the statistically valid Plackett-Burman design of experiments, the results revealed a maximum removal of both Pb(II) (96.3 %) and Cu(II) (96.42 %) followed by Cd(II) (80.04 %) and Zn(II) (71.3 %) at the end of the 60-h culture period. Further, the removal of these metals was attributed to both passive biosorption and accumulation by the actively growing N. muscorum biomass. Besides, the specific removal rate of these metals by N. muscorum was negatively correlated to its specific growth rate. For a better understanding of the effect of these metals on each other's removal by the cyanobacteria, the results were statistically analyzed in the form of analysis of variance (ANOVA) and Student's t test. ANOVA of the metal bioremoval revealed that the main (individual) effect due to the metals was highly significant (P value <0.05) on each other's removal. Student's t test results revealed that both Zn(II) and Pb(II) strongly inhibited both Cu(II) removal (P value <0.01) and Cd(II) removal (P value <0.02). All these results not only demonstrated a very good potential of the cyanobacteria in the bioremoval of these metals but also the effect of individual metals on each other's removal in the multicomponent system.