Adjuvant therapy may improve overall survival in high-risk periampullary adenocarcinomas patients - A match-pair analysis from a multi-institutional cohort study (The MIPPAP study).

BACKGROUND: The role of adjuvant therapy in resected periampullary adenocarcinomas is equivocal due to contrasting data and limited prospective trials. METHODS: The Multicentre Indian Pancreatic & Periampullary Adenocarcinoma Project (MIPPAP), included data from 8 institutions across India. Of the 1679 pancreatic resections, 736 patients with T3/T4 and/or Node positive adenocarcinomas (considered as high risk for recurrence) were included for analysis. Three (adjuvant): one (observation) matching, using T3/T4 T staging, nodal positivity and ampullary subtype was performed by using the nearest neighbour matching method. RESULTS: Of 736 patients eligible for inclusion, 621 patients were matched of which 458 patients received adjuvant therapy (AT) (predominantly gemcitabine-based) and 163 patients were observed (O). With a median follow-up of 42 months, there was a statistical difference in overall survival in favour of patients receiving AT as compared to those on observation [68.7 months vs. 61.1 months, Hazard ratio: 0.73 (95% CI: 0.54-0.97); p = 0.03]. Besides AT, presence of nodal involvement (median OS: 65.4 months vs not reached; p = 0.04) predicted for inferior OS. CONCLUSIONS: The results of the match-pair analysis suggest that adjuvant therapy improves overall survival in periampullary adenocarcinomas at high risk of recurrence with a greater benefit in T3/T4, node-positive and ampullary subtypes.

Norfloxacin salts of carboxylic acids curtail planktonic and biofilm mode of growth in ESKAPE pathogens.

AIMS: To enhance the antimicrobial and antibiofilm activity of norfloxacin against the planktonic and biofilm mode of growth in ESKAPE pathogens using chemically modified norfloxacin salts. METHODS AND RESULTS: Antimicrobial testing, synergy testing and time-kill curve analysis were performed to evaluate antibacterial effect of norfloxacin carboxylic acid salts against ESKAPE pathogens. In vivo efficacy to reduce bacterial bioburden was evaluated in zebrafish infection model. Crystal violet assay and live-dead staining were performed to discern antibiofilm effect. Membrane permeability, integrity and molecular docking studies were carried out to ascertain the mechanism of action. The carboxylic acid salts, relative to parent molecule norfloxacin, displayed two- to fourfold reduction in minimum inhibitory concentration against Staphylococcus aureus and Pseudomonas aeruginosa, in addition to displaying potent bacteriostatic effect against certain members of ESKAPE pathogens. In vivo treatments revealed that norfloxacin tartrate (SRIN2) reduced MRSA bioburden by greater than 1 log fold relative to parent molecule in the muscle tissue. In silico docking with gyrA of S. aureus showed increased affinity of SRIN2 towards DNA gyrase. The enhanced antibacterial effect of norfloxacin salts could be partially accounted by altered membrane permeability in S. aureus and perturbed membrane integrity in P. aeruginosa. Antibiofilm studies revealed that SRIN2 (norfloxacin tartrate) and SRIN3 (norfloxacin benzoate) exerted potent antibiofilm effect particularly against Gram-negative ESKAPE pathogens. The impaired colonization of both S. aureus and P. aeruginosa due to improved norfloxacin salts was further supported by live-dead imaging. CONCLUSION: Norfloxacin carboxylic acid salts can act as potential alternatives in terms of drug resensitization and reuse. SIGNIFICANCE AND IMPACT OF THE STUDY: Our study shows that carboxylic acid salts of norfloxacin could be effectively employed to treat both planktonic- and biofilm-based infections caused by select members of ESKAPE pathogens.

Voice source characterization using pitch synchronous discrete cosine transform for speaker identification.

A characterization of the voice source (VS) signal by the pitch synchronous (PS) discrete cosine transform (DCT) is proposed. With the integrated linear prediction residual (ILPR) as the VS estimate, the PS DCT of the ILPR is evaluated as a feature vector for speaker identification (SID). On TIMIT and YOHO databases, using a Gaussian mixture model (GMM)-based classifier, it performs on par with existing VS-based features. On the NIST 2003 database, fusion with a GMM-based classifier using MFCC features improves the identification accuracy by 12% in absolute terms, proving that the proposed characterization has good promise as a feature for SID studies.

Detection of the closure-burst transitions of stops and affricates in continuous speech using the plosion index.

Automatic and accurate detection of the closure-burst transition events of stops and affricates serves many applications in speech processing. A temporal measure named the plosion index is proposed to detect such events, which are characterized by an abrupt increase in energy. Using the maxima of the pitch-synchronous normalized cross correlation as an additional temporal feature, a rule-based algorithm is designed that aims at selecting only those events associated with the closure-burst transitions of stops and affricates. The performance of the algorithm, characterized by receiver operating characteristic curves and temporal accuracy, is evaluated using the labeled closure-burst transitions of stops and affricates of the entire TIMIT test and training databases. The robustness of the algorithm is studied with respect to global white and babble noise as well as local noise using the TIMIT test set and on telephone quality speech using the NTIMIT test set. For these experiments, the proposed algorithm, which does not require explicit statistical training and is based on two one-dimensional temporal measures, gives a performance comparable to or better than the state-of-the-art methods. In addition, to test the scalability, the algorithm is applied on the Buckeye conversational speech corpus and databases of two Indian languages.

Estimation of voice-onset time in continuous speech using temporal measures.

This paper proposes an automatic acoustic-phonetic method for estimating voice-onset time of stops. This method requires neither transcription of the utterance nor training of a classifier. It makes use of the plosion index for the automatic detection of burst onsets of stops. Having detected the burst onset, the onset of the voicing following the burst is detected using the epochal information and a temporal measure named the maximum weighted inner product. For validation, several experiments are carried out on the entire TIMIT database and two of the CMU Arctic corpora. The performance of the proposed method compares well with three state-of-the-art techniques.

Two-stage cytoreductive surgery for 'extensive' pseudomyxoma peritonei: A legitimate alternative or salvage strategy?

BACKGROUND AND AIM: Two-stage cytoreductive surgery (CRS) has been proposed as an alternative to one-stage surgery in patients who have 'extensive' pseudomyxoma peritonei (PMP) and/or are unfit for very extensive surgery, to reduce morbidity. We review current evidence on two-stage CRS focusing on patient selection, interval between procedures, extent of surgery, use of HIPEC, perioperative and oncological outcomes. METHODS: This is a narrative review. A literature search on PubMed and Embase was performed using keywords- 'Two-stage cytoreductive surgery', 'pseudomyxoma peritonei', 'high-volume PMP', 'huge PMP', 'cytoreductive surgery', 'HIPEC', 'staged surgery' and 'extensive pseudomyxoma peritonei'. RESULTS: Five studies reported outcomes in a total of 114 patients. The indications for two-stage CRS were: in two studies, patients undergoing an incomplete cytoreduction due to undue surgical risk were reevaluated for a second surgery during routine surveillance; severe comorbidities in one; extensive disease with PCI>28 in another and in one, only HIPEC was performed as a second procedure due to intraoperative hemodynamic instability (the two-stage procedure was performed in interest of patient's safety). Major morbidity ranged from 0 to 37.5 % (first-stage) and 25%-38.9 % (second-stage). Short term follow-up demonstrated equivalent short-term oncological outcomes compared to historical data. Long term follow-up and quality-of-life data were not available. CONCLUSIONS: The published studies showed different interpretations and applications of the two-stage CRS concept. The reported morbidity was similar to that after single-stage CRS for extensive PMP. Though short-term survival outcomes are acceptable, long-term follow-up is needed. Planned two-stage CRS should currently be reserved for highly selected clinical situations.

Modality-Specific Feature Selection, Data Augmentation and Temporal Context for Improved Performance in Sleep Staging.

This work attempts to design an effective sleep staging system, making the best use of the available signals, strategies, and features in the literature. It must not only perform well on different datasets comprising healthy and clinical populations but also achieve good accuracy in cross-dataset experiments. Toward this end, we propose a model comprising multiple binary classifiers in a hierarchical fashion, where, at each level, one or more of EEG, EOG, and EMG are selected to best differentiate between two sleep stages. The best set of 100 features is chosen out of all the features derived from selected signals. The class imbalance in data is addressed by random undersampling and boosting techniques with decision trees as weak learners. Temporal context and data augmentation are used to improve the performance. We also evaluate the performance of our model by training and testing on different datasets. We compare the results of five approaches: using only EEG, EEG+EOG, EEG+EMG+EOG, EEG+EMG, and selective modality with a specific combination of EEG, EMG, and/or EOG at each level. The best results are obtained by considering features from EEG+EMG+EOG at each hierarchical level. The proposed model achieves average accuracies of 83.1%, 90.0%, 84.4%, 82.1%, 81.5%, 79.9%, and 73.7% on Sleep-EDF, Exp Sleep-EDF, ISRUC-S1, S2 and S3, DRMS-SUB, and DRMS-PAT datasets, respectively. For all the datasets except DRMS-SUB, the proposed method outperforms all the state-of-the-art approaches. Cross-dataset performance exceeds 80% for all datasets except DRMS-PAT; independent of whether the test data is from normal subjects or patients.

Ethnic differences in vitamin D status, bone and body composition in South Asian indian and caucasian men.

Background: High prevalence of metabolic abnormalities and poor bone health in ethnic minorties may stem from differences in body composition and alterations in endocrine milieu. South Asian Indians (SAIs) are at greater risk for metabolic syndrome (MetS) and poor bone health than Caucasians. Often these differences are reported later in life and/or in a resident immigrant population compared to a Caucasian population. In this study, we determined whether vitamin D status, bone, body composition differed in young SAIs and Caucasians. Notably we compared differences amongst recent SAI immigrants and Caucasians. Methods: We examined differences in bone density, body composition, serum 25-hydroxy vitamin D (s25(OH)D), parathyroid hormone (sPTH), vitamin D binding protein (sDBP), osteocalcin (sOC), and dietary intakes in young healthy SAI and Caucasian men. Results: Sixty men (N = 30 SAIs and N = 30 Caucasians) with a mean age of 27.8 ± 7.4 years completed the study. Compared to the Caucasians, SAIs had statistically significantly lower s25(OH)D and higher sPTH (p < 0.05). We also found that s25(OH)D was negatively associated with sPTH only among the SAIs (r = - 0.389, p = 0.037). Also, lean mass% (LM%) and fat-free mass% (FFM%) were lower in SAIs (p < 0.05) compared to caucasians. s25(OH)D correlated with nearly all body composition parameters, while sPTH correlated negatively with LM% and FFM%, and positively with FM% (all p < 0.05) in the Caucasian group. Bone mineral density at most sites were also significantly lower (p < 0.05) in the SAI's compared to caucasians. Conclusion: Young SAIs have a poor vitamin D status and less favorable bone and body composition parameters compared to Caucasians. These findings highlight the possible complex interplay between skeletal and metabolic health in different ethnicities which may be evident early on in life. Interventions to improve bone and metabolic health should therefore target younger ethnic minorities.

Variational mode decomposition-based EEG analysis for the classification of disorders of consciousness.

Aberrant alterations in any of the two dimensions of consciousness, namely awareness and arousal, can lead to the emergence of disorders of consciousness (DOC). The development of DOC may arise from more severe or targeted lesions in the brain, resulting in widespread functional abnormalities. However, when it comes to classifying patients with disorders of consciousness, particularly utilizing resting-state electroencephalogram (EEG) signals through machine learning methods, several challenges surface. The non-stationarity and intricacy of EEG data present obstacles in understanding neuronal activities and achieving precise classification. To address these challenges, this study proposes variational mode decomposition (VMD) of EEG before feature extraction along with machine learning models. By decomposing preprocessed EEG signals into specified modes using VMD, features such as sample entropy, spectral entropy, kurtosis, and skewness are extracted across these modes. The study compares the performance of the features extracted from VMD-based approach with the frequency band-based approach and also the approach with features extracted from raw-EEG. The classification process involves binary classification between unresponsive wakefulness syndrome (UWS) and the minimally conscious state (MCS), as well as multi-class classification (coma vs. UWS vs. MCS). Kruskal-Wallis test was applied to determine the statistical significance of the features and features with a significance of p < 0.05 were chosen for a second round of classification experiments. Results indicate that the VMD-based features outperform the features of other two approaches, with the ensemble bagged tree (EBT) achieving the highest accuracy of 80.5% for multi-class classification (the best in the literature) and 86.7% for binary classification. This approach underscores the potential of integrating advanced signal processing techniques and machine learning in improving the classification of patients with disorders of consciousness, thereby enhancing patient care and facilitating informed treatment decision-making.

Predictors of distress associated with altered appearance and function in people treated surgically for oral cancers: a cross-sectional study.

This cross-sectional study was performed to examine sources of variation in distress associated with altered appearance and fundamental functions in oral cancer patients at 2 months, 12-15 months, 24-36 months, and ≥ 5 years post-definitive treatment. Eligible patients completed six scales from the FACE-Q Head and Neck Cancer Module. Pre-specified regression models were used to examine sources of variation in study outcomes for 145 patients. Patient self-reports indicated that distress associated with altered appearance and fundamental functions was highly variable, and distress was present beyond 5 years post-definitive treatment in some patients. Associations between distress scores and time post-definitive treatment, reconstructive surgery, and adjuvant therapy were not statistically significant. There was, however, moderate to strong evidence against the null hypothesis of no association between eating distress scores and sex, primary cancer site, and T-stage; smiling distress scores and age and primary cancer site; appearance distress scores and geographical remoteness and primary cancer site; and speaking distress scores and primary cancer site. Primary cancer site was the only significant independent predictor of multiple distress scores. These findings suggest that predicting the psychological impact of oral cancer treatment remains a challenge for the multidisciplinary team. Screening and interventions for psychological distress are essential beyond the preoperative and acute care settings.

Low cost calibrated mechanical noisemaker for hearing screening of neonates in resource constrained settings.

BACKGROUND & OBJECTIVES: There is a need to develop an affordable and reliable tool for hearing screening of neonates in resource constrained, medically underserved areas of developing nations. This study valuates a strategy of health worker based screening of neonates using a low cost mechanical calibrated noisemaker followed up with parental monitoring of age appropriate auditory milestones for detecting severe-profound hearing impairment in infants by 6 months of age. METHODS: A trained health worker under the supervision of a qualified audiologist screened 425 neonates of whom 20 had confirmed severe-profound hearing impairment. Mechanical calibrated noisemakers of 50, 60, 70 and 80 dB (A) were used to elicit the behavioural responses. The parents of screened neonates were instructed to monitor the normal language and auditory milestones till 6 months of age. This strategy was validated against the reference standard consisting of a battery of tests - namely, auditory brain stem response (ABR), otoacoustic emissions (OAE) and behavioural assessment at 2 years of age. Bayesian prevalence weighted measures of screening were calculated. RESULTS: The sensitivity and specificity was high with least false positive referrals for 70 and 80 dB (A) noisemakers. All the noisemakers had 100 per cent negative predictive value. 70 and 80 dB (A) noisemakers had high positive likelihood ratios of 19 and 34, respectively. The probability differences for pre- and post- test positive was 43 and 58 for 70 and 80 dB (A) noisemakers, respectively. INTERPRETATION & CONCLUSIONS: In a controlled setting, health workers with primary education can be trained to use a mechanical calibrated noisemaker made of locally available material to reliably screen for severe-profound hearing loss in neonates. The monitoring of auditory responses could be done by informed parents. Multi-centre field trials of this strategy need to be carried out to examine the feasibility of community health care workers using it in resource constrained settings of developing nations to implement an effective national neonatal hearing screening programme.

Prognostic factors in patients with node-negative gastric cancer: an Indian experience.

BACKGROUND: The status of the regional nodes is the most important prognostic factor in gastric cancer. There are subgroups of patients with different prognosis even in node-negative patients of gastric cancer. The aim of this study is to analyze the factors influencing the prognosis in Indian patients with node-negative gastric cancer. METHODS: This was a retrospective analysis of patients who underwent radical gastrectomy in a tertiary cancer centre in India between 1991 and 2007. The study group included only patients with histologically node-negative disease. Various clinical, pathological and treatment related factors in this group of patients were analyzed to determine their prognostic ability by univariate and multivariate analyses. RESULTS: Among the 417 patients who underwent gastrectomy during this period, 122 patients had node-negative disease. A major proportion of the patients had advanced gastric cancer. The 5-year overall survival and disease-free survival in all node-negative gastric cancer patients was 68.2% and 67.5% respectively. The overall recurrence rate in this group was 27.3%. On univariate analysis, the factors found to significantly influence the disease-free survival were the size, location and presence or absence of serosal invasion of the primary tumor. However, on multivariate analysis, only tumor size more than 3 cm and serosal invasion were found to be independently associated with an inferior survival. CONCLUSION: Serosal invasion and primary tumor size more than 3 cm independently predict a poor outcome in patients with node-negative gastric cancer.

Midline EEG Functional Connectivity As Biomarker for Conscious States in Sleep and Wakefulness.

Functional connectivity (FC) between different cortical regions of the brain has long been hypothesized to be necessary for conscious states in several modeling and empirical studies. The work presented herein estimates the FC between two bipolar midline electroencephalogram (EEG) recordings to evaluate its utility in discriminating consciousness levels across wakefulness and sleep. Consciousness levels were defined as Low, Medium, and High depending upon the ability of a subject to self-report their experiences at a later stage. The sleep EDF [expanded] dataset available in the Physionet data repository was used for analyses. FC was estimated using the debiased estimator of the squared Weighted Phase Lag Index (dWPLI2) metric. A total of 40 features extracted from the FC spectra for 10 EEG sub-bands were considered. FC trends demonstrated the highest alpha synchrony in the 'Low' conscious state. While the 'Medium' conscious state demonstrated superior phase synchronization in the low-gamma band, the 'High' conscious state was characterized by comparatively lower phase synchronization in all frequency bands. A Multi-Layer Perceptron (MLP) framework using a combination of 7 features yielded the highest cross-validation accuracy of 95.15% in distinguishing these conscious states. The study results provide a pertinent validation for the hypothesis that midline EEG FC is a reliable and robust signature of conscious states in sleep and wakefulness.

Brain Functional Connectivity as Biomarker for Propofol-Induced Alterations of Consciousness.

Understanding neural correlates of consciousness and its alterations poses a grand challenge for modern neuroscience. Even though recent years of research have shown many conceptual and empirical advances, the evolution of a system that can track anesthesia-induced loss of consciousness is hindered by the lack of reliable markers. The work presented herein estimates the functional connectivity (FC) between 21 scalp electroencephalogram (EEG) recordings to evaluate its utility in characterizing changes in brain networks during propofol sedation. The sedation dataset in the University of Cambridge data repository was used for analyses. FC was estimated using the debiased estimator of the squared Weighted Phase Lag Index (dWPLI2). Spectral FC networks before, during, and after sedation was considered for 5 EEG sub-bands. Results demonstrated significantly higher alpha band FC during baseline, mild and moderate sedation, and recovery stages. A striking association between frontal brain activity and propofol-sedation was also noticed. Furthermore, inhibition of frontal to parietal and frontal to occipital connections were observed as characteristic features of propofol-induced alterations in consciousness. A random subspace ensemble framework using logistic model tree as the base classifier, and 18 functional connections as features, yielded a cross-validation accuracy of 98.75% in discriminating baseline, mild and moderate sedation, and recovery stages. These findings validate that EEG-based FC can reliably distinguish altered conscious states associated with anaesthesia.

Increased Entropy of Gamma Oscillations in the Frontal Region during Meditation.

Meditation practices are considered mental training and have increasingly received attention from the scientific community due to their potential psychological and physical health benefits. We compared the EEG data recorded from long-term rajayoga practitioners during different meditative and non-meditative periods. Minimum variance modified fuzzy entropy (MVMFE) is computed for each EEG band for all channels of a given lobe. The means across all the channel entropy values were obtained and compared during meditative and non-meditative states. Meditators showed higher frontal entropy in the lower gamma band (25-45Hz) during the meditative states. Independent component analysis was applied to ensure that muscle or eye artifacts did not contribute to the gamma activity. Our results extend previous findings on the changes in entropy observed in long-term meditators during rajayoga practice. Gamma band in EEG is implicated in cognitive processes requiring high-level processing such as attention, learning, memory control, and retrieval. Gamma activity is also suggested as a potential biomarker for therapeutic progress in patients with clinical depression. Based on our findings, there is an excellent possibility to utilize the practice of meditation as a training tool to strengthen the neural circuits, where age-related degeneration is making its pathological impact.

Postoperative pulmonary complications following major head and neck cancer surgery.

The primary aim of this observational study was to describe the incidence of postoperative pulmonary complications (PPCs) in 60 consecutive, surgically treated head and neck cancer patients requiring free flap reconstruction and tracheostomy, using both a prospective and a retrospective outcome measure. Secondary aims were to identify risk factors for PPC development, explore the effects of PPC on outcomes, and describe the provision of postoperative physiotherapy in this population. Postoperative pulmonary complications occurred in nine (15%) patients based on the Melbourne Group Scale and 27 (45%) patients based on Health Information Service coding data. The occurrence of a PPC was not statistically correlated with age, smoking history, comorbidities, operative time, or type of resection or free flap. Patients who developed a PPC, compared to those who did not, had a higher preoperative body mass index (P=0.022) and were more likely to be sat out of bed earlier post-surgery (P=0.038). Overall, patients required a median of 9.0 (interquartile range 7.0-11.0) physiotherapy sessions. Patients developing a PPC required significantly more physiotherapy sessions (P=0.007) and additional days of supplemental oxygen (P=0.022) as compared to those without a PPC, despite a similar hospital length of stay. In future, targeted physiotherapy interventions may reduce PPCs in this population.

Classification of Phonological Categories in Imagined Speech using Phase Synchronization Measure.

Phonological categories in articulated speech are defined based on the place and manner of articulation. In this work, we investigate whether the phonological categories of the prompts imagined during speech imagery lead to differences in phase synchronization in various cortical regions that can be discriminated from the EEG captured during the imagination. Nasal and bilabial consonant are the two phonological categories considered due to their differences in both place and manner of articulation. Mean phase coherence (MPC) is used for measuring the phase synchronization and shallow neural network (NN) is used as the classifier. As a benchmark, we have also designed another NN based on statistical parameters extracted from imagined speech EEG. The NN trained on MPC values in the beta band gives classification results superior to NN trained on alpha band MPC values, gamma band MPC values and statistical parameters extracted from the EEG.Clinical relevance: Brain-computer interface (BCI) is a promising tool for aiding differently-abled people and for neurorehabilitation. One of the challenges in designing speech imagery based BCI is the identification of speech prompts that can lead to distinct neural activations. We have shown that nasal and blilabial consonants lead to dissimilar activations. Hence prompts orthogonal in these phonological categories are good choices as speech imagery prompts.

Intubation outcomes using the aerosol box during the COVID-19 pandemic: A prospective, observational study.

BACKGROUND AND AIMS: The aerosol box (AB), an improvised device used during the coronavirus disease (COVID)-19 pandemic, has attracted both interest and controversy. Several simulated studies have examined its protective efficacy as well as intubation efficiency. The aim of this study was to evaluate the practical conduct of intubation using the AB in patients undergoing elective, oncological surgery during the pandemic. METHODS: This prospective, observational study included adult patients undergoing oncological surgery. Thirteen anaesthesiologists performed 132 intubations using one of three ABs designated as AB 1, AB 2 and AB 3. The primary outcome was the difference in the time to intubation (TTI) between patients with Mallampati score MP I-II (Group 1) and MP III-IV (Group 2). Secondary outcomes included first-pass success rate, fall in peripheral oxygen saturation to < 95%, total number of attempts and failure to intubate using the AB. RESULTS: The mean TTI was not significantly different in Group 1 and Group 2 (71.02 (61.66) s vs. 101.35 (121.94) s respectively, P = 0.119). Desaturation during intubation was seen in 20 patients (15.1%). First pass success rate was achieved in 109 patients (82.6%). Twenty-one patients (15.9%) needed more than one attempt to intubate and the box had to be removed in 8 patients (6.1%) for facilitating intubation. The Mallampati score did not significantly influence either desaturation or first pass success rate. CONCLUSION: There was a non-significant increasing TTI trend in patients with a higher MP score with the use of an aerosol box. However, this did not translate to a clinically significant difference in the overall intubation outcomes.

Evaluation of the immunogenicity and protective efficacy of a recombinant CS6-based ETEC vaccine in an Aotus nancymaae CS6 + ETEC challenge model.

Colonization factors or Coli surface antigens (CFs or CS) are important virulence factors of Enterotoxigenic E. coli (ETEC) that mediate intestinal colonization and accordingly are targets of vaccine development efforts. CS6 is a highly prevalent CF associated with symptomatic ETEC infection both in endemic populations and amongst travelers. In this study, we used an Aotus nancymaae non-human primate ETEC challenge model with a CS6 + ETEC strain, B7A, to test the immunogenicity and protective efficacy (PE) of a recombinant CS6-based subunit vaccine. Specifically, we determined the ability of dscCssBA, the donor strand complemented recombinant stabilized fusion of the two subunits of the CS6 fimbriae, CssA and CssB, to elicit protection against CS6 + ETEC mediated diarrhea when given intradermally (ID) with the genetically attenuated double mutant heat-labile enterotoxin LT(R192G/L211A) (dmLT). ID vaccination with dscCssBA + dmLT induced strong serum antibody responses against CS6 and LT. Importantly, vaccination with dscCssBA + dmLT resulted in no observed diarrheal disease (PE = 100%, p = 0.03) following B7A challenge as compared to PBS immunized animals, with an attack rate of 62.5%. These data demonstrate the potential role that CS6 may play in ETEC infection and that recombinant dscCssBA antigen can provide protection against challenge with the homologous CS6 + ETEC strain, B7A, in the Aotus nancymaae diarrheal challenge model. Combined, these data indicate that CS6, and more specifically, a recombinant engineered derivative should be considered for further clinical development.

Quality assessment in magnetic resonance images.

Assessing quality of medical images is critical because the subsequent course of actions depend on it. Extensive use of clinical magnetic resonance (MR) imaging warrants a study in image indices used for MR images. The quality of MR images assumes particular significance in the determination of their reliability for diagnostics, response to therapies, synchronization across different imaging cycles, optimization of interventional imaging, and image restoration. In this paper, we review various techniques developed for the assessment of MR image quality. The reported quality indices can be broadly classified as subjective/objective, automatic/semi-automatic, region-of-interest/non-region-of-interest-based, full-reference/no-reference and HVS incorporated/non-HVS incorporated. The trade-of across the various indices lies in the computational complexity, assumptions, repeatability, and resemblance to human perception. Because images are eventually viewed by the human eye, it is found that it is important to incorporate aspects of human visual response, sensitivity, and characteristics in computing quality indices. Additionally, no-reference metrics are the most relevant due to the lack of availability of a golden standard against which images could be compared. Techniques that are objective and automatic are preferred for their repeatability and to eliminate avoidable errors due to factors like stress, which arise in human intervention.