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Ipomoea biloba is a plant, which belongs to Convolvulanceae family and also has more medicinal values. It is an aquatic creep runner plant used as a medical herb for various diseases such as asthma and rheumatism and dried leaves are used to apply for burns. In the current investigation, the Methanol extract of Ipomoea biloba leaves was experimented to evaluate the phytochemical properties, in vitro antioxidant activity and in vitro anticancer assay on MCF7 cell line. Plant components can be used to extract both hydrophilic and lipophilic compounds. Methanol is an excellent solvent of its polarity. Because methanol is highly volatile, we can remove the solvent by distillation at a low temperature after extraction. The leaves of Ipomoea biloba extract used in an analysis which shows the ability to produced estrogen comes froth as estradiol via estrogen receptor in the cytoplasm of the cell. Phytochemical screening showed the presence of carbohydrate, amino acids, alkaloids, saponin, steroids, terpenoids and phenols. The antioxidant activity of Methanol extract indicates the significant antioxidant content it and against with standard ascorbic acid. The results of this analysis highlight the interest of Ipomoea biloba extract for the isolation of anticancer molecules.

Occupational therapy (OT) enhances functional independence in the daily activities of people with disabilities and subsequently their quality of life. Research in OT generates evidence to provide safe and effective services to the disabled. However, OT research in India has been shown to have various methodological limitations. These methodological limitations are expected to impact the quality of OT research as well as the evidence derived from this research to inform decision-making in rehabilitation. The majority of the OT research is disseminated and promoted through the All India Occupational Therapists' Association's (AIOTA) annual national conference (ANC). Analyzing the abstracts, selected for the presentation at the ANC could help understand and strategically improve the quality of OT research in India.To explore and describe the quality of OT research in India.Descriptive analysis.Descriptive, nonsystematic review and analysis of the key methodological aspects of the conference abstracts submitted for the AIOTA ANC published in theAbout 218 abstracts had been selected for either poster or oral presentations in the AIOTA ANC. All the abstracts were included for the review. A total of 8055 participants were recruited for the studies conducted from 2017 to 2021. About 5757 (72%) of the participants were recruited for cross-sectional studies. Nearly 72 (33%) of the abstracts presented were related to cross-sectional studies, 52 (24%) were case studies and 66 (30%) were experimental studies. However, research designs implying highest level of evidence such as systematic reviews were only 4 (2%) and randomized controlled trials were only 9 (4%) with 297 participants. Notably, 203 (98%) of the all the studies evaluating effectiveness of interventions or aiming to investigate associations reported positive results with statistically significant improvements and associations.The review provides invaluable information related to the quality of OT research in India. It implies the need to improve the scientific rigor of the evidence generated in relation to OT research in India. This review also implies the need for a radical change and strengthening of OT research within OT education and professional practice in India. National and global OT associations need to prioritize good quality OT research by enhancing the research skills and competencies of OTs in India. This could help promote evidence-based OT science and develop the OT profession in the world's second-most populous country. In addition, it is also expected to encourage those OT researchers who have been striving to build OT research standards in India.

The current research objective is to explore changes in solar radiation variables across the Indian region during the lock-down period. Global Horizontal Irradiance (GHI), Direct Normal Irradiance ...

Wind power forecasting has gained significant attention due to advances in wind energy generation in power frameworks and the uncertain nature of wind. In this manner, to maintain an affordable, reliable, economical, and dependable power supply, accurately predicting wind power is important. In recent years, several investigations and studies have been conducted in this field. Unfortunately, these examinations disregarded the significance of data preprocessing and the impact of various missing values, thereby resulting in poor performance in forecasting. However, long short-term memory (LSTM) network, a kind of recurrent neural network (RNN), can predict and process the time-series data at moderately long intervals and time delays, thereby producing good forecasting results using time-series data. This article recommends a hybrid forecasting model for forecasting wind power to improve the performance of the prediction. An improved long short-term memory network-enhanced forget-gate network (LSTM-EFG) model, whose appropriate parameters are optimized using cuckoo search optimization algorithm (CSO), is used to forecast the subseries data that is extracted using ensemble empirical mode decomposition (EEMD). The experimental results show that the proposed forecasting model overcomes the limitations of traditional forecasting models and efficiently improves forecasting accuracy. Furthermore, it serves as an operational tool for wind power plants management.

Persons with Disabilities (PWD) experience unmet needs related to health, rehabilitation, education, livelihood, social participation, and empowerment, particularly those living in a resource-poor context such as in the Indian context. The same applies to Children with Disabilities (CWD) as well. Given the pandemic restrictions imposed by the government of India, the provision of therapeutic rehabilitation services for PWDs and CWDs has come to a deadlock. Therefore, the PWDs and the parents of CWDs are substantially impacted by the double contextual burden of demand and access to rehabilitation services in India. However, there has been some light at the end of this dark tunnel provided by the existing telecommunication strategies. Both parents/caregivers and rehabilitation service providers started to find a way out of this situation on their own in India by optimizing their skills and resources for telerehabilitation. However, adopting this strategy requires evidence. Hence a critical Strengths Weaknesses Opportunities and Threats (SWOT) analysis of the telerehabilitation strategy for empowering PWDs and CWDs in an Indian context is warranted and is of immense public health importance. A narrative review was conducted. Telerehabilitation has several strengths, weaknesses, opportunities, and threats. Telecommunication resources, Access to Rehabilitation services, Parent's and consumer acceptance, Service efficiency, and data documentation could be considered as strengths; Skills, Competencies, Opportunity cost, Resource intensiveness, Evidence for Effectiveness, Comprehensibility could be considered as weaknesses; Therapy innovations, Evidence generation, System strengthening and Capacity Building could be considered as opportunities; Patient Safety, Ethical Integrity, Data security, and Professional practice insecurity could be considered as potential Threats to Telerehabilitation. Telerehabilitation has considerable scope for providing meaningful therapeutic experience and hastens the process of rehabilitation of CWDs in the current context. The SWOT and its implications must be kept in mind to ensure that CWDs receive the best quality continuum of care in the present context with utmost ethical and evidence-based considerations. This could bridge the gaps in access to rehabilitation services with sustainable solutions than patchy temporary solutions that are not sustainable.

Persons with Disabilities (PWD) experience unmet needs related to health, rehabilitation, education, livelihood, social participation, and empowerment, particularly those living in a resource-poor context such as in the Indian context. The same applies to Children with Disabilities (CWD) as well. Given the pandemic restrictions imposed by the government of India, the provision of therapeutic rehabilitation services for PWDs and CWDs has come to a deadlock. Therefore, the PWDs and the parents of CWDs are substantially impacted by the double contextual burden of demand and access to rehabilitation services in India. However, there has been some light at the end of this dark tunnel provided by the existing telecommunication strategies. Both parents/caregivers and rehabilitation service providers started to find a way out of this situation on their own in India by optimizing their skills and resources for telerehabilitation. However, adopting this strategy requires evidence. Hence a critical Strengths Weaknesses Opportunities and Threats (SWOT) analysis of the telerehabilitation strategy for empowering PWDs and CWDs in an Indian context is warranted and is of immense public health importance. A narrative review was conducted.Telerehabilitation has several strengths, weaknesses, opportunities, and threats. Telecommunication resources, Access to Rehabilitation services, Parent’s and consumer acceptance, Service efficiency, and data documentation could be considered as strengths; Skills, Competencies, Opportunity cost, Resource intensiveness, Evidence for Effectiveness, Comprehensibility could be considered as weaknesses; Therapy innovations, Evidence generation, System strengthening and Capacity Building could be considered as opportunities; Patient Safety, Ethical Integrity, Data security, and Professional practice insecurity could be considered as potential Threats to Telerehabilitation. Telerehabilitation has considerable scope for providing meaningful therapeutic experience and hastens the process of rehabilitation of CWDs in the current context. The SWOT and its implications must be kept in mind to ensure that CWDs receive the best quality continuum of care in the present context with utmost ethical and evidence-based considerations. This could bridge the gaps in access to rehabilitation services with sustainable solutions than patchy temporary solutions that are not sustainable.

The energy density of a supercapacitor is largely reliant on functional parameters of electrode material and electrolyte. To improve the energy density of the CuS asymmetric device, optimization of sulfur concentration (thiourea) in the precursor and identification of the most suited electrolyte have been attempted. The changes in thiourea concentration greatly affect the physical and electrochemical features of CuS. The highest specific capacitance of 298 F g−1 at 2 A g−1 was obtained for copper sulfide nanoparticles prepared with 1:2 ratio of copper acetate and thiourea (C3). It exhibits excellent cycling stability in 2 M KOH electrolyte. In addition, to evaluate the most suited electrolyte, electrochemical studies were performed with different electrolytes (H2SO4, Na2SO4, KOH and LiClO4 in propylene carbonate). Based on the electrochemical results, it was found that an outstanding performance has originated from H2SO4 electrolyte (773 F g−1 at 2 A g−1). The C3 electrode exhibits no perceptible degradation in capacity even after 4000 charge-discharge cycles in acidic electrolyte. Further, for real-life applications, an asymmetric device was fabricated using C3 as a cathode and PVA/ H2SO4 as electrolyte. The device attained a highest energy density of 21 W h kg−1 at a power density of 310 W kg−1. Furthermore, lighting up of red and yellow LEDs is demonstrated using the fabricated asymmetric device. The efficient device performances concluded that C3 is a potential cathode material for future supercapacitor applications.

Wind speed plays a predominant role in the wind energy system. Forecasting of long-term wind speed in certain diverse areas, for example, the optimal design of wind farms, energy management, and restructured electricity markets has always been a popular spot for research. A reliable wind speed forecast can mitigate the errors in scheduling and, in effect, improve the stability of the electrical grid power and reduce the ancillary service costs of the energy market. Short-term updates are being considered as less important to few sites than reliability and reasonable start-up time, while successful long-term forecasting wants not only a trigger of present events but similarly in-depth knowledge of historical patterns of seasonal winds and site-specific weather parameters. This paper describes insight into the leading forecasting models, related to wind speed and wind power, and depends on numerical weather prediction (NWP) models, statistical methods, artificial neural network (ANN) models, and hybrid models over various time horizons. This chapter also discussed an overview of the comparative study of several available forecasting models. However, this paper discusses the key challenges and problems related to the forecasting of wind speed. This survey addresses the existing functionality measures for various approaches by splitting them into different techniques: time-series models, artificial intelligence models, and hybrid models. All these methods include specific models that are explored with different parameters, benefits, and disadvantages.

The combination of big data and deep learning is a world-shattering technology that can make a great impact on any industry if used in a proper way. With the availability of large volume of health care datasets and progressions in deep learning techniques, systems are now well equipped in diagnosing many health problems. Utilizing the intensity of substantial historical information in electronic health record (EHR), we built up, a conventional predictive temporal model utilizing recurrent neural systems (RNN) like LSTM and connected to longitudinal time stepped EHR. Experience records were contribution to RNN to anticipate the analysis and prescription classes for a resulting visit during heart disappointment (e.g. diagnosis codes, drug codes or method codes). In this paper, we also investigated whether use of deep learning to model temporal relations among events in electronic health records (EHRs) would enhance the model performance in predicting initial diagnosis of heart failure (HF) compared to some of the traditional methods that disregard temporality. By examining these time stamped EHRs, we could recognize the associations between various diagnosis occasions and finally predicate when a patient is being analyzed for a disease. In any case, it is hard to access the current EHR data straightforwardly, since almost all data are sparse and not standardized. Along these lines, we proposed a robust model for prediction of heart failure. The fundamental commitment of this paper is to predict the failure of heart by means of a neural network model based on patient's electronic medicinal information. In order to, demonstrate the diagnosis events and prediction of heart failure, we used the medical concept vectors and the essential standards of a long short-term memory (LSTM) deep network model. The proposed LSTM model uses SiLU and tanh as activation function in the hidden layers and Softmax in output layer in the network. Bridgeout is used as a regularization technique for weight optimization throughout the network. Assessments subject to the real-time data exhibit the favorable effectiveness and feasibility of recommended model in the risk of heart failure prediction. The results showed improved accuracy in heart failure detection and the model performance is compared using the existing deep learning models. Enhanced prior detection could expose novel chances for deferring or anticipating movement to analysis of heart failure and diminish cost.

The combination of big data and deep learning is a world-shattering technology that can make a great impact on any industry if used in a proper way. With the availability of large volume of health care datasets and progressions in deep learning techniques, systems are now well equipped in diagnosing many health problems. Utilizing the intensity of substantial historical information in electronic health record (EHR), we built up, a conventional predictive temporal model utilizing recurrent neural systems (RNN) like LSTM and connected to longitudinal time stepped EHR. Experience records were contribution to RNN to anticipate the analysis and prescription classes for a resulting visit during heart disappointment (e.g. diagnosis codes, drug codes or method codes). In this paper, we also investigated whether use of deep learning to model temporal relations among events in electronic health records (EHRs) would enhance the model performance in predicting initial diagnosis of heart failure (HF) compared to some of the traditional methods that disregard temporality. By examining these time stamped EHRs, we could recognize the associations between various diagnosis occasions and finally predicate when a patient is being analyzed for a disease. In any case, it is hard to access the current EHR data straightforwardly, since almost all data are sparse and not standardized. Along these lines, we proposed a robust model for prediction of heart failure. The fundamental commitment of this paper is to predict the failure of heart by means of a neural network model based on patient's electronic medicinal information. In order to, demonstrate the diagnosis events and prediction of heart failure, we used the medical concept vectors and the essential standards of a long short-term memory (LSTM) deep network model. The proposed LSTM model uses SiLU and tanh as activation function in the hidden layers and Softmax in output layer in the network. Bridgeout is used as a regularization technique for weight optimization throughout the network. Assessments subject to the real-time data exhibit the favorable effectiveness and feasibility of recommended model in the risk of heart failure prediction. The results showed improved accuracy in heart failure detection and the model performance is compared using the existing deep learning models. Enhanced prior detection could expose novel chances for deferring or anticipating movement to analysis of heart failure and diminish cost.

Solar irradiance forecasting will turn into a major challenge in the future integration of solar energy resources into existing structures of energy supply. There are squeezing requirements for app...

Background: The current evidence for occupational therapy practice, teaching, and research is replicated and implemented significantly from high-income countries in India. Therefore, a systematic review and an evaluation of existing evidence for occupational therapy (OT) to reduce disabilities including impairments, activity limitations, and participation restriction in persons with disabilities (PWD) in India are warranted. Objectives: The objective of this review was to evaluate the effectiveness of OT interventions for reducing disabilities in PWD in India. Study Design: Systematic review. Methods: We searched the Cochrane CENTRAL, MEDLINE, Embase, CINAHL, PsycINFO, AMED, and Web of Science. A hand search was also carried out in selected Indian journals, OT-specific databases, and repositories, such as Indian Journal of Occupational Therapy, Indian Journal of Physiotherapy and Occupational Therapy, OT Seekers, World Federation of Occupational Therapy Bulletin, Asia Pacific Occupational Therapists Regional Group, and clinical trials registers. The search was restricted to published studies conducted in India during 2000–2021. We included randomized controlled trials (RCTs) of an occupational therapy intervention delivered by OTs for PWD, where the effects of the intervention were evaluated using any relevant disability outcome measure. Studies without access to full text were excluded. Two review authors independently completed screening, and one author reviewed the full text of the screened studies. Another pair of authors extracted data from included studies for prespecified disability outcomes, and two authors assessed the risk of bias in the included studies. Results: We identified seven RCTs of occupational therapy interventions for PWD in India with 305 participants in total. All seven studies were very different in terms of their objective, participants, comparison, and outcomes. Allocation concealment and blinding and risk of bias were high in five trials. All the trials reported impairment outcomes with a statistically significant difference between the experimental arm and the control arm in terms of their primary outcomes except one. Given the sample size and the risk of bias in each of the included trials, the effect size has to be understood and interpreted with utmost caution. Conclusion: Overall, this review establishes the paucity of evidence for occupational therapy for PWD in India. Building the capacity for rigorous and relevant scientific research in occupational therapy would enable bridging the gaps in evidence for occupational therapy in India.

We are in the middle stage era of the digital revolution where all consumers and businesses demand decisions to be based on evidence collected from data. The challenge of big data is mainly well defined in the health and bioinformatics space where, for example, whole genome sequencing (WGS) technology leads researchers to cross-examine nearly all three billion base pairs of the human genome. When compared to traditional big data disciplines, such as astronomy, Facebook, YouTube and Twitter, data acquisition in this genomic space is predicted to outpace more, and nearly 50% of the world’s population would be aimed for a medical decision by 2030. As of now, the analysis of using medical genomics data is having the front position of this growing need to apply complicated machine learning techniques to large high-dimensional datasets. Biology is even more complicated than this genomic space. In this paper, we explored the parallel algorithm for random forests with large genomic data, which is classified to find the ethnicity nature of a human being. Apache Spark’s MLlib is mainly designed for the common use cases where the genomics data have nearly thousands of features and that need to be scaled up for the millions of genomic features. The study is conducted using VariantSpark based on Spark core, and it has the parallelization computing to coordinate with genomic massively distributed machine learning job.

Introduction: Value addition of millet is an important strategy to improve its utilization and the method of processing determines the quality characteristics of the value added products. Utilization of millets in food formulations is increasing worldwide, since they are rich sources of phytochemicals and dietary fiber which offer several health benefits. Objective: Thus in the present study, an attempt was made to develop roasted and pressure cooked barnyard millet flour incorporated chapatti at different levels and its consumer acceptability to evaluate the total polyphenol content and in vitro antioxidant activity. Materials and methods: Roasted and pressure cooked barnyard millet flour incorporated chapattis were developed at four different levels i.e., 10%, 30%, 50% and 100% millet flour to wheat flour. Sensory evaluation was done for all the developed products using 9 point hedonic scale. For the best variation, the total polyphenol content was estimated using folic ciocalteau method and the in vitro antioxidant activity were evaluated on the basis of measuring Ferric reducing ability power (FRAP) and scavenging activity for DPPH radicals by methanolic extracts. Results: Roasted and pressure cooked barnyard millet flour incorporation in the preparation of chapatti at 10 per cent level was acceptable. The total polyphenol content of best variation (10%) of roasted and pressure cooked barnyard millet flour incorporated chapatti was 6.12 mg/g and 5.38 mg/g respectively. The DPPH radical scavenging activity and Ferric reducing ability power (FRAP) of the same was found to be 59% and 13.42 mg/g and 53% and 11.57 mg/g respectively. The standard chapatti prepared with wheat flour was found to have 4.02 mg/g of polyphenol content, 47% of DPPH radical scavenging activity and 9.84 mg/g of Ferric reducing ability power. Conclusion: Thus the present study concludes that roasting and pressure cooking enhances the polyphenol content of barnyard millet grains which might also contribute significantly to the management and/or prevention of degenerative diseases associated with free radical damage due to its high polyphenol content and antioxidant activity.

Introduction: Millet grains, before consumption and for preparing food, are usually processed by commonly used traditional processing techniques to improve their edible, nutritional and sensory properties. Background: The processing techniques aim to increase the physicochemical accessibility of micronutrients, decrease the content of anti-nutrients or increase the content of compounds that improve bioavailability. Objectives: Thus, an attempt was made in the present study with the objectives to study the effects of boiling, pressure cooking, roasting and germination on functional, nutritional, anti-nutritional and pasting properties of barnyard millet and foxtail millet. Materials and Methods: Physical properties of unprocessed millets, chemical, functional, anti-nutritional and pasting properties of both unprocessed and processed millets were analysed using standard techniques. Results and Conclusion: Physical characteristics such as thousand grain weight, thousand grain volumes, hydration capacity and index, swelling capacity and index and cooking quality of the selected two unprocessed millets were considerably differed from each other. There was a significant variation with respect to functional, nutritional, anti-nutritional and pasting properties of selected two millets in response to different processing methods. Among them, germination reduces the anti-nutritional factors while roasting significantly increases the nutritional compounds. The improved functional and pasting properties of the selected two millets were observed in the germinated and roasted millet flours.

This paper presents a chat application that make use of speech as interaction mode between users in their mother tongue. This system is based on Machine Translation (MT) which automatically translates a sentence from one language to other. Now-a-days, Chat has become increasingly important as an effective means to communicate in social networks. But in existing personal chat systems, chat services are text-based only. To circumvent this issue, innovative, practical XMPP-based chat system architecture is proposed in this paper that creates a Speech based chat services by integrating state-of-the-art technologies like Automatic Speech Recognition (ASR), Text to Speech (TTS) and Machine Translation (MT). The proposed system eliminates the need to have a common language for chat communication. In this chat system the user speak in their own language after selecting the language of their comfort. The audio will be recognized in real time. The text is translated to the language opted by end user and the system synthesizes it. Here, we discuss an interface prototype that can overcome the language barrier to some extent.

Lipomas are benign mesenchymal neoplasms of soft tissue that can be found in any part of the human body. Conversely, their presence in the oral mucosa is rather uncommon, with approximately 4% of the cases occurring in the oral cavity. The aim of this paper is to present the importance of diagnosis and treatment of intraoral lipoma. Clinicians must be able to recognize rare lesions, like intraoral lipomas, to provide appropriate treatment, thereby ensuring comfort and quality of life for the patient.

Speech corpora are vital resource in development and evaluation of automatic speech recognition systems, as well as for acoustic phonetic studies. Collecting a huge corpus is not an easy task. The lack of such resources is one of the reasons for the absence of good quality speech recognition systems in Indian languages. Here we have automated such process by developing web based tool for collecting broad band speech data and an IVR system with speech recognition for collecting narrow band speech data. The main features includes the full support for the typical recording, annotation and project administration workflow, easy editing of the speech content, with an advantage of a fully localizable user interface. This paper describes in detail the development of web based speech collection tool and an IVR system which will enable end-to-end building of speech corpus with minimum manual effort.

This paper describes the development of state-of-the-art large vocabulary continuous speech recognition (LVCSR) system for the Malayalam language with an application for visually challenged. For an LVCSR, building a high accurate acoustic models and large-scale language models are the challenging task. Speech corpus for training the system is collected from 80 native speakers in room environment ensuring the speaker variance. Mel-frequency Cepstral Coefficients (MFCC) method is used as a front-end to extract acoustic features from the input signal. Acoustic model is built on 30 hours of speech data based on Hidden Markov Model (HMM). A hybrid model, integrating rule based and statistical method is used to handle pronunciation variations in the dictionary. The best configuration of the system achieved word accuracy of 75% in average. Accuracy of the system is further increased up to 80% in average, by implementing speaker adaptation technique. The developed system is integrated to OpenOffice Writer together with TTS for making it user friendly editor for visually challenged people.

This paper describes the implementation of a dictation system for Malayalam office documents in OpenOffice Writer. Dictation system is built using state-of-the-art large vocabulary continuous speech recognition system for the Malayalam language. This system supports a vocabulary of 5000 most commonly used office domain words and is employed with a vocabulary updating facility to handle out-of-vocabulary words. The system is based on Hidden Markov Model (HMM), trained with huge (25 hours) amount of data. The training data is collected in room environment, ensuring the speaker variance and the phonetic richness. A hybrid model which integrates the rule based method with statistical method is used to handle the pronunciation variations for the creation of the pronunciation dictionary. The system is first of its kind which simplifies the tedious task of typing in Malayalam. Apart from dictating office documents with 75 ±5 % accuracy, the system is equipped with a facility of suggestion generation by which the user will be provided with alternate words for mis-recognized words. The system also supports some basic voice command operations for file operations like open, save, close etc. This system has an option to adapt to the user's voice which will improve the recognition accuracy by 2-5%. The system is successfully implemented in OpenOffice Writer and tested.

The effect of vegetarian diets on faecal microbiota has been explored largely through culture-based techniques. The present study compared the faecal microbiota of vegetarian and omnivorous young women in southern India. Faecal samples were obtained from thirty-two lacto-vegetarian and twenty-four omnivorous young adult women from a similar social and economic background. Macronutrient intake and anthropometric data were collected. Faecal microbiota of interest was quantified by real-time PCR with SYBR Green using primers targeting 16S rRNA genes of groups, including: Clostridium coccoides group (Clostridium cluster XIVa), Roseburia spp.–Eubacterium rectale, Bacteroides–Prevotella group, Bifidobacterium genus, Lactobacillus group, Clostridium leptum group (Clostridium cluster IV), Faecalibacterium prausnitzii, Ruminococcus productus–C. coccoides, Butyrivibrio, Enterococcus species and Enterobacteriaceae. The groups were matched for age, socio-economic score and anthropometric indices. Intake of energy, complex carbohydrates and Ca were significantly higher in the omnivorous group. The faecal microbiota of the omnivorous group was enriched with Clostridium cluster XIVa bacteria, specifically Roseburia–E. rectale. The relative proportions of other microbial communities were similar in both groups. The butyryl-CoA CoA-transferase gene, associated with microbial butyrate production, was present in greater amounts in the faeces of omnivores, and the levels were highly correlated with Clostridium cluster XIVa and Roseburia–E. rectale abundance and to a lesser extent with Clostridium leptum and F. prausnitzii abundance and with crude fibre intake. Omnivores had an increased relative abundance of Clostridium cluster XIVa bacteria and butyryl-CoA CoA-transferase gene compared with vegetarians, but we were unable to identify the components of the diet responsible for this difference.

Background Probiotics are used to provide health benefits. The present study tested the effect of a probiotic yoghurt on faecal output of beta-defensin and immunoglobulin A in a group of young healthy women eating a defined diet. Findings 26 women aged 18-21 (median 19) years residing in a hostel were given 200 ml normal yoghurt every day for a week, followed by probiotic yoghurt containing Bifidobacterium lactis Bb12® (109 in 200 ml) for three weeks, followed again by normal yoghurt for four weeks. Stool samples were collected at 0, 4 and 8 weeks and assayed for immunoglobulin A and human beta-defensin-2 by ELISA. All participants tolerated both normal and probiotic yoghurt well. Human beta-defensin-2 levels in faeces were not altered during the course of the study. On the other hand, compared to the basal sample, faecal IgA increased during probiotic feeding (P = 0.0184) and returned to normal after cessation of probiotic yoghurt intake. Conclusions Bifidobacterium lactis Bb12® increased secretory IgA output in faeces. This property may explain the ability of probiotics to prevent gastrointestinal and lower respiratory tract infections.

Fe deficiency in women contributes significantly to maternal and child morbidity in India. The intestinal bacterial flora may facilitate absorption of Fe from the caecum and proximal colon. The present study investigated the possibility that intestinal microbiota of anaemic young women may differ from that of women with normal Hb levels. The microbiota was quantified by real-time PCR in faeces of eight anaemic (Hb ≤ 100 g/l) and twenty-six normohaemic (Hb ≥ 120 g/l) women aged 18–25 years. Sequences of 16S ribosomal DNA (rDNA) specific to Bifidobacterium genus, Lactobacillus acidophilus group, Bacteroides–Prevotella–Porphyromonas group, Clostridium leptum group and Eubacterium rectale were amplified and expressed (as relative difference) relative to the universally conserved bacterial 16S rDNA sequences. Dietary intakes of energy, carbohydrate, fibre and Fe were ascertained by maintenance of a diet diary for a week. Faecal lactobacilli were significantly lower in anaemic women (median 6·6 × 10− 8, relative difference compared with total bacteria) than in the reference group (2·9 × 10− 6; P = 0·001, unpaired t test with logarithmic transformation). There was no difference between the two groups with respect to any of the other bacteria that were examined. Intakes of energy, carbohydrate, fibre, Fe and milk were similar in both the groups. Fe deficiency in young women in south India was associated with low levels of lactobacilli in the faeces. The relationship between lactobacilli and Fe deficiency needs to be explored further.

A 29-year-old man with extrahepatic portal vein obstruction who underwent variceal eradication by sclerotherapy six years ago, was admitted with hypotension and abdominal pain. Abdominal paracentesis yielded frank blood. Laparotomy showed bleeding from a large ectopic vessel along the splenorenal ligament. The vessel was ligated and the patient recovered.