Measuring health care continuum with multifaceted indicators for people who use drugs in Vietnam.

Continuum of care is a concept that has been widely applied in HIV prevention and treatment studies. However, measuring care continuum can be challenging because it involves multiple stages and multiple components or domains at each stage of care. In this study, we introduced an analytical framework to (1) estimate intervention effects overall and by domain using a multi-level modeling approach, and (2) learn possible patterns of domains over time utilizing a multi-layer heatmap visualization. Longitudinal data from an intervention study conducted among people who use drugs in Vietnam were used to construct Seek, Test, Treat, and Retain (STTR) domain and overall scores. Findings from the adjusted analysis showed that people who use drugs in the intervention exhibited a significantly greater improvement in the overall STTR score than those in the control (p-values < .0001). The multi-layer heatmap revealed different patterns of the individual domains over time and the inter-relationships among the individual domains. This study demonstrates the feasibility of constructing a general fulfillment score and domain specific scores to measure care continuum among people who use drugs. The analytical framework can be readily extended to evaluate service fulfillment outcomes in health services and treatment studies for other key populations.

Incidence and Geographical Distribution of Adrenocortical Carcinoma: Retrospective Analysis of a State Cancer Registry.

OBJECTIVE: Adrenocortical carcinoma (ACC) is a rare malignancy without established association with environmental risk factors. ACC incidence is stable based on large surgical databases while referral centers data reported increasing number of cases seen. We studied ACC incidence and distribution at a county level to find potential ACC "hot spots" that could be linked to environmental exposures. METHODS: A retrospective analysis of Texas Cancer Registry that included ACC patients diagnosed between 2000 and 2018. County-level heatmaps were created and compared with breast, prostate, and lung cancer. RESULTS: We identified 448 ACC cases during the study period. Cases were registered in 110 of the 254 counties (43.3%) in Texas, representing 92.74% of the total population. The median incidence was 23 new cases/y (range 14-33). The mean population-adjusted ACC incidence rate was 0.104 per 100 000 per year (standard deviation 0.005; 95% CI, 0.092-0.116). Seven counties (6.3%) accounted for 215 (48.0%) cases, with more than 10 cases each and median standardized incidence ratio (SIR) of 0.1 (range, 0.0-0.9). One hundred three counties (93.7%) accounted for the remaining 233 cases (52%), with fewer than 10 cases per county. The highest standardized incidence ratios were found in counties with a median population of fewer than 14 000 residents and with only one reported case. CONCLUSION: Our analysis is the first report to create ACC heatmap and could not detect any geographic clustering of ACC in Texas. The incidence of ACC remained stable and consistent with data from other large databases.

Rule-based deep learning method for prognosis of neonatal hypoxic-ischemic encephalopathy by using susceptibility weighted image analysis.

OBJECTIVE: Susceptibility weighted imaging (SWI) of neonatal hypoxic-ischemic brain injury can provide assistance in the prognosis of neonatal hypoxic-ischemic encephalopathy (HIE). We propose a convolutional neural network model to classify SWI images with HIE. MATERIALS AND METHODS: Due to the lack of a large dataset, transfer learning method with fine-tuning a pre-trained ResNet 50 is introduced. We randomly select 11 datasets from patients with normal neurology outcomes (n = 31) and patients with abnormal neurology outcomes (n = 11) at 24 months of age to avoid bias in classification due to any imbalance in the data. RESULTS: We develop a rule-based system to improve the classification performance, with an accuracy of 0.93 ± 0.09. We also compute heatmaps produced by the Grad-CAM technique to analyze which areas of SWI images contributed more to the classification patients with abnormal neurology outcome. CONCLUSION: Such regions that are important in the classification accuracy can interpret the relationship between the brain regions affected by hypoxic-ischemic and neurodevelopmental outcomes of infants with HIE at the age of 2 years.

Associations Between Noticing E-Cigarette Advertising Features and E-Cigarette Appeal and Switching Interest Among Young Adult Dual Users.

BACKGROUND: E-cigarette advertising, which often includes various features, may prompt e-cigarette use and product switching. This study examined the associations between noticing e-cigarette ad features and perceived product appeal and interest in completely switching from cigarettes to advertised e-cigarettes among young adult dual users of both products. METHODS: We analyzed data from an online heatmap experiment among young adult dual users defined as established cigarette smokers who currently used e-cigarettes (ages 18-34 years; n = 1,821). Participants viewed 12 e-cigarette ads, clicked on ad features (e.g., fruit flavors, nicotine warnings, price promotions, smoker-targeted claims) that attracted their attention (defined as "noticing"), and answered questions about e-cigarette product appeal and interest in completely switching from cigarettes to the e-cigarettes shown. We examined within-person associations between noticing specific ad features and outcomes, controlling for demographic and tobacco use-related characteristics. RESULTS: Noticing fruit flavors (AOR = 1.67 and 1.28) and fruit images (AOR = 1.53 and 1.21) was positively associated with having any e-cigarette product appeal and switching interest. Noticing price promotions (AOR = 1.23) was positively associated with product appeal. In contrast, noticing nicotine warnings (AOR = 0.74 and 0.86), smoker-targeted claims (AOR = 0.78 and 0.89), and tobacco flavors (AOR = 0.92 and 0.90) was negatively associated with product appeal and switching interest. CONCLUSIONS: Noticing certain e-cigarette ad features (e.g., fruit flavors and nicotine warnings) may be associated with product appeal and/or switching interest among young adult dual users. More research is needed to assess the influence of e-cigarette ad features that promote product switching interests among cigarette smokers while discourage interests among tobacco-naïve individuals.

Application of the Semi-Supervised Learning Approach for Pavement Defect Detection.

Road surface quality is essential for driver comfort and safety, making it crucial to monitor pavement conditions and detect defects in real time. However, the diversity of defects and the complexity of ambient conditions make it challenging to develop an effective and robust classification and detection algorithm. In this study, we adopted a semi-supervised learning approach to train ResNet-18 for image feature retrieval and then classification and detection of pavement defects. The resulting feature embedding vectors from image patches were retrieved, concatenated, and randomly sampled to model a multivariate normal distribution based on the only one-class training pavement image dataset. The calibration pavement image dataset was used to determine the defect score threshold based on the receiver operating characteristic curve, with the Mahalanobis distance employed as a metric to evaluate differences between normal and defect pavement images. Finally, a heatmap derived from the defect score map for the testing dataset was overlaid on the original pavement images to provide insight into the network's decisions and guide measures to improve its performance. The results demonstrate that the model's classification accuracy improved from 0.868 to 0.887 using the expanded and augmented pavement image data based on the analysis of heatmaps.

Vehicle Occupant Detection Based on MM-Wave Radar.

With the continuous development of automotive intelligence, vehicle occupant detection technology has received increasing attention. Despite various types of research in this field, a simple, reliable, and highly private detection method is lacking. This paper proposes a method for vehicle occupant detection using millimeter-wave radar. Specifically, the paper outlines the system design for vehicle occupant detection using millimeter-wave radar. By collecting the raw signals of FMCW radar and applying Range-FFT and DoA estimation algorithms, a range-azimuth heatmap was generated, visually depicting the current status of people inside the vehicle. Furthermore, utilizing the collected range-azimuth heatmap of passengers, this paper integrates the Faster R-CNN deep learning networks with radar signal processing to identify passenger information. Finally, to test the performance of the detection method proposed in this article, an experimental verification was conducted in a car and the results were compared with those of traditional machine learning algorithms. The findings indicated that the method employed in this experiment achieves higher accuracy, reaching approximately 99%.

Impact of semolina-barley mixture on the volatolomic profile of dough and pasta: characterization by a multivariate chemometric approach.

BACKGROUND: Barley flour, known to be rich in various phytochemicals, has been demonstrated to improve the technological and nutritional properties of pasta; however, its volatile profile, on which its aromatic properties depend, also plays an important role in the acceptance of barley-enriched pasta. In the present work, volatile organic compounds (VOCs) of semolina doughs enriched with different percentages of barley and of the related pasta were characterized by solid phase micro-extraction (HS-SPME) coupled to gas-chromatography/mass spectrometry (GC-MS), and evaluated using a multivariate statistical approach, including principal component analysis (PCA), cluster heatmaps, Pearson's and Spearman's correlations, and partial least squares correlation (PLSC). RESULTS: The effects of single raw materials, and their interactions, were studied to establish their importance in the volatile profile of the samples, and the correlation between the dough VOCs and the processed product VOCs was assessed. The presence of barley flour markedly affected the volatile profile in comparison with the dough obtained with only durum wheat. For alcohols, esters, terpenes, and some aldehydes there was a clear correlation with the percentage of barley. For some of the VOCs, on the other hand, a strong dependence on the ingredients interaction effect due to the mixing stage has been demonstrated. CONCLUSION: The heatmaps allowed a good graphical visualization of the relationship between molecules and barley percentage, offering the possibility to select the best one according to the desired volatolomic footprint. Pasta with 40% of barley was demonstrated to give pasta with the most complex volatile profile. © 2024 Society of Chemical Industry.

Real-Time 3D Object Detection on Crowded Pedestrians.

In the field of autonomous driving, object detection under point clouds is indispensable for environmental perception. In order to achieve the goal of reducing blind spots in perception, many autonomous driving schemes have added low-cost blind-filling LiDAR on the side of the vehicle. Unlike point cloud target detection based on high-performance LiDAR, the blind-filling LiDARs have low vertical angular resolution and are mounted on the side of the vehicle, resulting in easily mixed point clouds of pedestrian targets in close proximity to each other. These characteristics are harmful for target detection. Currently, many research works focus on target detection under high-density LiDAR. These methods cannot effectively deal with the high sparsity of the point clouds, and the recall and detection accuracy of crowded pedestrian targets tend to be low. To overcome these problems, we propose a real-time detection model for crowded pedestrian targets, namely RTCP. To improve computational efficiency, we utilize an attention-based point sampling method to reduce the redundancy of the point clouds, then we obtain new feature tensors by the quantization of the point cloud space and neighborhood fusion in polar coordinates. In order to make it easier for the model to focus on the center position of the target, we propose an object alignment attention module (OAA) for position alignment, and we utilize an additional branch of the targets' location occupied heatmap to guide the training of the OAA module. These methods improve the model's robustness against the occlusion of crowded pedestrian targets. Finally, we evaluate the detector on KITTI, JRDB, and our own blind-filling LiDAR dataset, and our algorithm achieved the best trade-off of detection accuracy against runtime efficiency.

Explainable Automated TI-RADS Evaluation of Thyroid Nodules.

A thyroid nodule, a common abnormal growth within the thyroid gland, is often identified through ultrasound imaging of the neck. These growths may be solid- or fluid-filled, and their treatment is influenced by factors such as size and location. The Thyroid Imaging Reporting and Data System (TI-RADS) is a classification method that categorizes thyroid nodules into risk levels based on features such as size, echogenicity, margin, shape, and calcification. It guides clinicians in deciding whether a biopsy or other further evaluation is needed. Machine learning (ML) can complement TI-RADS classification, thereby improving the detection of malignant tumors. When combined with expert rules (TI-RADS) and explanations, ML models may uncover elements that TI-RADS misses, especially when TI-RADS training data are scarce. In this paper, we present an automated system for classifying thyroid nodules according to TI-RADS and assessing malignancy effectively. We use ResNet-101 and DenseNet-201 models to classify thyroid nodules according to TI-RADS and malignancy. By analyzing the models' last layer using the Grad-CAM algorithm, we demonstrate that these models can identify risk areas and detect nodule features relevant to the TI-RADS score. By integrating Grad-CAM results with feature probability calculations, we provide a precise heat map, visualizing specific features within the nodule and potentially assisting doctors in their assessments. Our experiments show that the utilization of ResNet-101 and DenseNet-201 models, in conjunction with Grad-CAM visualization analysis, improves TI-RADS classification accuracy by up to 10%. This enhancement, achieved through iterative analysis and re-training, underscores the potential of machine learning in advancing thyroid nodule diagnosis, offering a promising direction for further exploration and clinical application.

Heatmap-Guided Selective Feature Attention for Robust Cascaded Face Alignment.

Face alignment methods have been actively studied using coordinate and heatmap regression tasks. Although these regression tasks have the same objective for facial landmark detection, each task requires different valid feature maps. Therefore, it is not easy to simultaneously train two kinds of tasks with a multi-task learning network structure. Some studies have proposed multi-task learning networks with two kinds of tasks, but they do not suggest an efficient network that can train them simultaneously because of the shared noisy feature maps. In this paper, we propose a heatmap-guided selective feature attention for robust cascaded face alignment based on multi-task learning, which improves the performance of face alignment by efficiently training coordinate regression and heatmap regression. The proposed network improves the performance of face alignment by selecting valid feature maps for heatmap and coordinate regression and using the background propagation connection for tasks. This study also uses a refinement strategy that detects global landmarks through a heatmap regression task and then localizes landmarks through cascaded coordinate regression tasks. To evaluate the proposed network, we tested it on the 300W, AFLW, COFW, and WFLW datasets and obtained results that outperformed other state-of-the-art networks.

Improving Small-Scale Human Action Recognition Performance Using a 3D Heatmap Volume.

In recent years, skeleton-based human action recognition has garnered significant research attention, with proposed recognition or segmentation methods typically validated on large-scale coarse-grained action datasets. However, there remains a lack of research on the recognition of small-scale fine-grained human actions using deep learning methods, which have greater practical significance. To address this gap, we propose a novel approach based on heatmap-based pseudo videos and a unified, general model applicable to all modality datasets. Leveraging anthropometric kinematics as prior information, we extract common human motion features among datasets through an ad hoc pre-trained model. To overcome joint mismatch issues, we partition the human skeleton into five parts, a simple yet effective technique for information sharing. Our approach is evaluated on two datasets, including the public Nursing Activities and our self-built Tai Chi Action dataset. Results from linear evaluation protocol and fine-tuned evaluation demonstrate that our pre-trained model effectively captures common motion features among human actions and achieves steady and precise accuracy across all training settings, while mitigating network overfitting. Notably, our model outperforms state-of-the-art models in recognition accuracy when fusing joint and limb modality features along the channel dimension.

Anatomical variations of the internal jugular vein in the context of central line placement: A visual approach to data processing.

Central line placement in the internal jugular vein (IJV) can result in complications. Previous studies that examined variations in geometric anatomical parameters in pediatric populations have reduced these risks in children. The aim of this study was to establish possible anatomical variations to improve central line placement in the adult population and demonstrate the use of a correlation heatmap in processing large amounts of data. Twenty-seven volunteers were imaged using ultrasound at three different neck levels on right and left sides and various anatomical parameters were measured. Demographic information was also collected and included in the data processing. The Pearson coefficient was derived from each possible relationship between the measured parameters and was plotted in a correlation heatmap. Strong correlations were observed between the body mass index and the depth of the IJV and common carotid artery (CCA), the relative depth of the IJV to the CCA. No significant correlations were found in the dimensions of both vessels. Anatomical variations were more common than anticipated and should be taken into account when performing central line catheterization in order to minimize post-procedure complications.

Phytochemical Screening on Phenolic, Flavonoid Contents, and Antioxidant Activities of Six Indigenous Plants Used in Traditional Thai Medicine.

The antioxidant activity of a traditional Thai formula has been studied and compared to each plant. The formula comprised the roots of Caesalpinia digyna Rottler, Huberantha cerasoides (Roxb.) Benth), Oxyceros horridus Lour, Antidesma ghaesembilla Gaerth, Combretum quadrangulare Kurz, and Ziziphus cambodiana Pierre. The stem was also studied in comparison. The ethanolic extract from each plant part and the mixed plants mimicking the traditional formula were prepared and investigated for antioxidant capability in vitro via DPPH radical scavenging and ferric-reducing antioxidant power assays. The phytochemical constituents were determined by chemical screening, total phenolic (TPC) and flavonoid contents (TFC), and high-performance liquid chromatography. The relationship between antioxidant activity and the contributed phytochemicals was determined using correlation analysis and principal component analysis (PCA). Results showed that extracts from both parts of the plant formula showed the highest antioxidant activity compared to a single plant extract. Among the six plants, C. digyna exhibited the highest TPC and antioxidant activity. TPC had a strong positive correlation with antioxidant activity. PCA revealed that gallic acid contributed to the antioxidant activity. In conclusion, the ethanolic extracts of the traditional formula and C. digyna have the potential for further chemical characterization and study related to antioxidant activity.

Putting infection on the map: Using heatmaps to characterise within- and between-host distributions of trematode metacercariae.

The location of parasites within individual hosts is often treated as a static trait, yet many parasite species can occur in multiple locations or organs within their hosts. Here, we apply distributional heat maps to study the within- and between-host infection patterns for four trematodes (Alaria marcianae, Cephalogonimus americanus, Echinostoma spp. and Ribeiroia ondatrae) within the amphibian hosts Pseudacris regilla and two species of Taricha. We developed heatmaps from 71 individual hosts from six locations in California, which illustrate stark differences among parasites both in their primary locations within amphibian hosts as well as their degree of location specificity. While metacercariae (i.e., cysts) of two parasites (C. americanus and A. marcianae) were relative generalists in habitat selection and often occurred throughout the host, two others (R. ondatrae and Echinostoma spp.) were highly localised to a specific organ or organ system. Comparing parasite distributions among these parasite taxa highlighted locations of overlap showing potential areas of interactions, such as the mandibular inner dermis region, chest and throat inner dermis and the tail reabsorption outer epidermis. Additionally, the within-host distribution of R. ondatrae differed between host species, with metacercariae aggregating in the anterior dermis areas of newts, compared with the posterior dermis area in frogs. The ability to measure fine-scale changes or alterations in parasite distributions has the potential to provide further insight about ecological questions concerning habitat preference, resource selection, host pathology and disease control.

On-court throwing activity of male handball players during the European Championship 2020.

The aim of this study was to investigate the on-court throwing activity in regards to playing positions, throwing zones on the court and throwing velocity category during the male European Championship 2020. A local positioning system with microsensors placed both in the shirts of the players and inside the ball itself was used. In total, 6568 throws were retrieved for analysis from the entire tournament. Results showed that first-line players (wings and line players) used their natural zone more to throw (65% left wing, 60% right wing, and 97% line player), showing higher effectiveness from there (p < 0.05). Second lines players distributed more of their throws (45% left back, 50% right back and 32% center back in their natural zones) giving priority to the throws in 6 m, where they showed higher effectiveness (p < 0.05), or between the lines. Considering all players, shots from nearby and intermediate areas corresponded to 83% of the shots showing higher efficiency (p < 0.05) than shots performed from the areas furthest from the goal (14.9%. from zones 6-7-8). Back players mainly performed the highest velocity throws (Category 4 > ~100 km/h) from these furthest areas. Throwing velocity and effectiveness by throwing zones and positions was not significantly modified over the three rounds of the tournament (p > 0.05), indicating no effect of possible fatigue. A higher team ranking was associated with higher throwing efficiency but only for wing players. The results of this research could help handball coaches to better adjust training programs for the improvement of throwing velocity and its transfer to the competition.

A multi-robot deep Q-learning framework for priority-based sanitization of railway stations.

Sanitizing railway stations is a relevant issue, primarily due to the recent evolution of the Covid-19 pandemic. In this work, we propose a multi-robot approach to sanitize railway stations based on a distributed Deep Q-Learning technique. The proposed framework relies on anonymous data from existing WiFi networks to dynamically estimate crowded areas within the station and to develop a heatmap of prioritized areas to be sanitized. Such heatmap is then provided to a team of cleaning robots - each endowed with a robot-specific convolutional neural network - that learn how to effectively cooperate and sanitize the station's areas according to the associated priorities. The proposed approach is evaluated in a realistic simulation scenario provided by the Italian largest railways station: Roma Termini. In this setting, we consider different case studies to assess how the approach scales with the number of robots and how the trained system performs with a real dataset retrieved from a one-day data recording of the station's WiFi network.

[Differences in chemical components in processing of dried ginger-steamed, sand-fried, and rice swill water-bleached Aconiti Lateralis Radix Praeparata pieces in "Jianchang" faction based on UPLC-MS/MS].

This study compared the changes in chemical components during the processing of different types of Aconiti Lateralis Radix Praeparata(ALRP) in "Jianchang" faction, i.e., dried ginger-steamed ALRP pieces(Yin-FP), sand-fried ALRP pieces(Yang-FP), and rice swill water-bleached ALRP pieces(DFP), and provided a scientific basis for the mechanism in toxicity reduction and efficacy enhancement from a compositional perspective. Samples were collected during the processing of the three types of ALRP pieces, yielding raw ALRP pieces, water-bleached Yin-FP, ginger juice-moistened Yin-FP, steamed Yin-FP, water-bleached Yang-FP, sand-fried Yang-FP, water-bleached DFP, rice swill water-bleached DFP, and roasted DFP. Aconitine, mesaconitine, hypaconitine, benzoylaconine, benzoylmesaconine, benzoylhypaconine, aconine, mesaconine, hypaconine, salsolinol, fuziline, and higenamine in the extracts were determined by UPLC-MS/MS, and then content analysis and cluster heatmap analysis were performed on 11 sets of samples. During the processing of the three types of ALRP pieces, bleaching significantly reduced the content of 12 alkaloids; steaming, stir-frying, and roasting significantly reduced the content of diester-type alkaloids(aconitine, mesaconitine, and hypaconitine) and significantly increased the content of monoester-type alkaloids(benzoylaconine, benzoylmesaconine, and benzoylhypaconine) and aminoalcohol-type alkaloids(aconine, mesaconine, and hypaconine). During the processing of Yin-FP, the diester-type alkaloids continuously decreased, while the monoester-type and aminoalcohol-type alkaloids showed an initial decrease followed by an increase. During the processing of Yin-FP, Yang-FP, and DFP, the diester-type alkaloids continuously decreased, while the monoester-type and aminoalcohol-type alkaloids showed an initial decrease followed by an increase. Steamed Yin-FP showed a higher increase in content than fried Yang-FP and roasted DFP. Comprehensive analysis of content differences in toxic and therapeutic components in three ALRP pieces suggests that the distinctive processing methods in "Jianchang" faction can indeed achieve detoxification and efficacy enhancement on ALRP. This study provides references for understanding the mechanisms of action of the three processing methods.

Phenotypic and mutational spectrum of ROR2-related Robinow syndrome.

Robinow syndrome is characterized by a triad of craniofacial dysmorphisms, disproportionate-limb short stature, and genital hypoplasia. A significant degree of phenotypic variability seems to correlate with different genes/loci. Disturbances of the noncanonical WNT-pathway have been identified as the main cause of the syndrome. Biallelic variants in ROR2 cause an autosomal recessive form of the syndrome with distinctive skeletal findings. Twenty-two patients with a clinical diagnosis of autosomal recessive Robinow syndrome were screened for variants in ROR2 using multiple molecular approaches. We identified 25 putatively pathogenic ROR2 variants, 16 novel, including single nucleotide variants and exonic deletions. Detailed phenotypic analyses revealed that all subjects presented with a prominent forehead, hypertelorism, short nose, abnormality of the nasal tip, brachydactyly, mesomelic limb shortening, short stature, and genital hypoplasia in male patients. A total of 19 clinical features were present in more than 75% of the subjects, thus pointing to an overall uniformity of the phenotype. Disease-causing variants in ROR2, contribute to a clinically recognizable autosomal recessive trait phenotype with multiple skeletal defects. A comprehensive quantitative clinical evaluation of this cohort delineated the phenotypic spectrum of ROR2-related Robinow syndrome. The identification of exonic deletion variant alleles further supports the contention of a loss-of-function mechanism in the etiology of the syndrome.

The Immune Deficiency and Dysregulation Activity (IDDA2.1 'Kaleidoscope') Score and Other Clinical Measures in Inborn Errors of Immunity.

Quantifying the phenotypic features of rare diseases such as inborn errors of immunity (IEI) helps clinicians make diagnoses, classify disorders, and objectify the disease severity at its first presentation as well as during therapy and follow-up. Furthermore, it may allow cross-sectional and cohort comparisons and support treatment decisions such as an evaluation for transplantation. On the basis of a literature review, we provide a descriptive comparison of ten selected scores and measures frequently used in IEI and divide these into three categories: (1) diagnostic tools (for Hyper-IgE syndrome, hemophagocytic lymphohistiocytosis, and Wiskott-Aldrich syndrome), (2) morbidity and disease activity measures (for common variable immune deficiency [CVID], profound combined immune deficiency, CTLA-4 haploinsufficiency, immune deficiency and dysregulation activity [IDDA], IPEX organ impairment, and the autoinflammatory disease activity index), and (3) treatment stratification scores (shown for hypogammaglobulinemia). The depth of preclinical and statistical validations varies among the presented tools, and disease-inherent and user-dependent factors complicate their broader application. To support a comparable, standardized evaluation for prospective monitoring of diseases with immune dysregulation, we propose the IDDA2.1 score (comprising 22 parameters on a 2-5-step scale) as a simple yet comprehensive and powerful tool. Originally developed for use in a retrospective study in LRBA deficiency, this new version may be applied to all IEI with immune dysregulation. Reviewing published aggregate cohort data from hundreds of patients, the IDDA kaleidoscope function is presented for 18 exemplary IEI as an instructive phenotype-pattern visualization tool, and an unsupervised, hierarchically clustered heatmap mathematically confirms similarities and differences in their phenotype expression profiles.

Probiotic potential and immunomodulatory properties in Enterococcus faecium GMB24 and Enterococcus hirae SMB16 isolated from goat and sheep milk.

Probiotic attributes of lactic acid bacteria isolated from goat and sheep milk samples were analysed by culturing them on an MRS agar medium. The most potential isolates, GMB24 and SMB16, were identified by biochemical tests which had ability to tolerate different concentrations of acid and bile and phenol resistance. They were further identified as Enterococcus faecium GMB24 and Enterococcus hirae SMB16 by 16S rRNA gene sequencing approach. The probiotic potential of the isolates GMB24 and SMB16 were recorded including antimicrobial activity against pathogenic bacteria viz., Escherichia coli (MTCC118), Staphylococcus aureus (MTCC7443), Pseudomonas aeruginosa (MTCC424), Listeria monocytogens (MTCC657) and Salmonella typhimurium (MTCC733), and antibiotic susceptibility test. The isolates SMB16 and GMB24 exhibited a higher zone of inhibition against P. aeruginosa (19.00 ± 0.57 mm) and S. aureus (25.66 ± 0.88 mm), respectively. The data from these experiments were used for the principal component analysis (PCA) to assess the survivability of the isolates under different factors. The heatmap generated in this study clustered the bacterial isolates based on their phenotype properties. Further, immunomodulating activities of these probiotic bacteria were tested on neutrophil adhesion test, haemagglutinating antibody titer and delayed-type hypersensitivity. Probiotic E. faecium GMB24 and E. hirae SMB16, at 109 cells/mL doses per day, increased the neutrophil adhesion, haemagglutinating antibody titer and DTH in comparison to the untreated control group. The isolates showed negative test for haemolytic and gelatinase activities and hence were considered safe. E. faecium GMB24 and E. hirae SMB16 were shown to have high probiotic potential and immune-stimulant action.