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Byline: S. Prabhakara, H. Hanumantha Rao, L. Prashanth, K. Anbazhagan, Rochette. Jacques, Basanth. Reddy To Editor, Paroxysmal kinesigenic dyskinesia (PKD) is an involuntary movement disorder belonging to the category of [...]

This article discusses the use of artificial intelligence (AI) systems to interpret blind sweep ultrasound scans for estimating gestational age (GA) in new clinical settings. The study involved adapting a previously trained AI model to a different probe manufacturer and evaluating its performance on blind sweeps from 824 patients. Without fine-tuning, the model's mean absolute error (MAE) for GA estimation was comparable to standard clinical estimates. However, after fine-tuning on a small number of scans, the model achieved similar performance to the original setting. These findings suggest that AI-based ultrasound assessment can be successfully deployed in new sites and expand access to care in low-resource settings. [Extracted from the article]

Byline: B. Chandrashekar, S. Prabhakara, T. Mohan, D. Shabeer, Basavaraj. Bhandare, M. Nalini, P. Sharmila, D. Meghana, Basanth. Reddy, H. Hanumantha Rao, H. Sahajananda, K. Anbazhagan OBJECTIVES: Rubia cordifolia L. [...]

Byline: S. Prabhakara, Kolandaswamy. Anbazhagan Paroxysmal kinesigenic dyskinesia (PKD) is an abnormal involuntary movement that is episodic or intermittent, with sudden onset, and the attacks are induced by sudden movement. [...]

Paroxysmal kinesigenic dyskinesia (PKD) is an abnormal involuntary movement that is episodic or intermittent, with sudden onset, and the attacks are induced by sudden movement. Mutations in proline-rich transmembrane protein 2 (PRRT2) gene have been implicated in the cause of this disorder. This study presents a case of PKD on the basis of clinical findings supported and evidences obtained through a mutational analysis. Sequencing of all the exons of PRRT2 gene revealed a frameshift mutation (p.R217Pfs*8) in exon 2 and a novel transition mutation (c.244C > T) in 5'-untranslated region (UTR). Though mutations in PRRT2 gene are well-established in PKD, this study for the first time presents a novel transition mutation in the exon 2 region. [ABSTRACT FROM AUTHOR]

Background & objectives: Temozolomide (TMZ), a second generation alkylating drug, an effective cytotoxic agent as well as radiosensitizer for malignant brain tumours, has side effects like myelosuppression. Lonidamine (LND) increases the effectiveness of several experimental multiple chemotherapy protocols, without increasing bone marrow toxicities and is effective in brain tumour patients. The objective of the present studies was to investigate whether combining clinically relevant doses of LND and TMZ could increase the proliferation and radiation response of malignant human brain tumour cells in vitro. Methods: A malignant human glioma (U373MG) cell line was used in these studies. TMZ (20, 41) or 6µM) or LND (100, 150 or 200 µM), or the combination of both (20 and 100 µM, respectively) in 0.1 per cent dimethyl sulphoxide (DMSO) were added three days after setting up cultures, in six well plates (5x104 cells/well). The effects of continuous treatment for two days on proliferation response and cytologist were studied after trypsinization; by cell counts and the uptake of trypan blue dye (0.5%). For the study of radiation (60Co-Gamma-rays, 2 Gy) response, drugs were removed 4 h after irradiation and cultures were grown further in drug free, normal growth medium for another 20 h or 44 h. Results: Continuous presence of TMZ or LND for two days significantly inhibited cell proliferation in a concentration dependent manner. The frequencies of non viable cells increased significantly only at higher concentrations of LND. Combination of 20 µM TMZ with 100 µM LND had additive effects on proliferation response, without affecting cell viability. Short-term drug treatments without irradiation did not induce micronuclei formation. Cell proliferation and viability were also not affected. However, post-irradiation presence of either of these drugs for 4 h significantly reduced the proliferation response, 24 and 48 h after treatments. It was further inhibited by the combination treatment. On the contrary, radiation induced micronuclei formation was enhanced by either of the drugs; which was significantly increased by the combined treatment, 24 h as well as 48 h after irradiation. No effects on cell viability were observed, immediately after these treatments as well as at later time points. Interpretation & conclusions: Our findings showed that combination of TMZ and LND at clinically achievable, low plasma concentrations could inhibit tumour growth, and Ionidamine could reduce the dose of temozolnmide required for radiosensitization of brain tumours. [ABSTRACT FROM AUTHOR]

The potentiality of Lentinus connatus, a new mushroom fungus identified in Tamil Nadu, India, for biomanure production from sugarcane trash (Saccharum officinarum L.), was studied in comparison with existing biodegrader Pleurotus sajor-caju. Lentinus connatus elaborated high lignocellulolytic enzymes and biomass production compared to P. sajor-caju. Pit method of composting with periodical turning, using L. connatus with calcium carbonate (activator) drastically reduced carbon:nitrogen (C:N) ratio. High macro- and micronutrients levels were recorded by this treatment. In the greenhouse, soil application of sugarcane trash biomanure at 18 t/ha and reduced level of urea at 13 kg N/ha significantly reduced the groundnut (Arachis hypogaea L.) root rot diseases incited by Thanatephorus cucumeris and Corticium rolfsii, compared to normal recommended doses of inorganic fertilizers. [ABSTRACT FROM AUTHOR]

Cardiovascular diseases (CVDs) are responsible for a large proportion of premature deaths in low- and middle-income countries. Early CVD detection and intervention is critical in these populations, yet many existing CVD risk scores require a physical examination or lab measurements, which can be challenging in such health systems due to limited accessibility. We investigated the potential to use photoplethysmography (PPG), a sensing technology available on most smartphones that can potentially enable large-scale screening at low cost, for CVD risk prediction. We developed a deep learning PPG-based CVD risk score (DLS) to predict the probability of having major adverse cardiovascular events (MACE: non-fatal myocardial infarction, stroke, and cardiovascular death) within ten years, given only age, sex, smoking status and PPG as predictors. We compare the DLS with the office-based refit-WHO score, which adopts the shared predictors from WHO and Globorisk scores (age, sex, smoking status, height, weight and systolic blood pressure) but refitted on the UK Biobank (UKB) cohort. All models were trained on a development dataset (141,509 participants) and evaluated on a geographically separate test (54,856 participants) dataset, both from UKB. DLS's C-statistic (71.1%, 95% CI 69.9-72.4) is non-inferior to office-based refit-WHO score (70.9%, 95% CI 69.7-72.2; non-inferiority margin of 2.5%, p<0.01) in the test dataset. The calibration of the DLS is satisfactory, with a 1.8% mean absolute calibration error. Adding DLS features to the office-based score increases the C-statistic by 1.0% (95% CI 0.6-1.4). DLS predicts ten-year MACE risk comparable with the office-based refit-WHO score. Interpretability analyses suggest that the DLS-extracted features are related to PPG waveform morphology and are independent of heart rate. Our study provides a proof-of-concept and suggests the potential of a PPG-based approach strategies for community-based primary prevention in resource-limited regions., Competing Interests: Author WHW, SB, MD, CC, SK, YL, and DA are employed at Google LLC and hold shares in Alphabet, and are co-inventors on patents (in various stages) for CVD risk prediction using deep learning and PPG, but declare no non-financial competing interests. LH, BB, CYM, YM, GSC, SS, SP are employed at Google LLC and hold shares in Alphabet but declare no non-financial competing interests. SK serves as an Associate Editor for this journal but had no role to play in the editorial process and decisions for this manuscript. GD declares no financial or non-financial competing interests., (Copyright: © 2024 Weng et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Purpose To evaluate the impact of an artificial intelligence (AI) assistant for lung cancer screening on multinational clinical workflows. Materials and Methods An AI assistant for lung cancer screening was evaluated on two retrospective randomized multireader multicase studies where 627 (141 cancer-positive cases) low-dose chest CT cases were each read twice (with and without AI assistance) by experienced thoracic radiologists (six U.S.-based or six Japan-based radiologists), resulting in a total of 7524 interpretations. Positive cases were defined as those within 2 years before a pathology-confirmed lung cancer diagnosis. Negative cases were defined as those without any subsequent cancer diagnosis for at least 2 years and were enriched for a spectrum of diverse nodules. The studies measured the readers' level of suspicion (on a 0-100 scale), country-specific screening system scoring categories, and management recommendations. Evaluation metrics included the area under the receiver operating characteristic curve (AUC) for level of suspicion and sensitivity and specificity of recall recommendations. Results With AI assistance, the radiologists' AUC increased by 0.023 (0.70 to 0.72; P = .02) for the U.S. study and by 0.023 (0.93 to 0.96; P = .18) for the Japan study. Scoring system specificity for actionable findings increased 5.5% (57% to 63%; P < .001) for the U.S. study and 6.7% (23% to 30%; P < .001) for the Japan study. There was no evidence of a difference in corresponding sensitivity between unassisted and AI-assisted reads for the U.S. (67.3% to 67.5%; P = .88) and Japan (98% to 100%; P > .99) studies. Corresponding stand-alone AI AUC system performance was 0.75 (95% CI: 0.70, 0.81) and 0.88 (95% CI: 0.78, 0.97) for the U.S.- and Japan-based datasets, respectively. Conclusion The concurrent AI interface improved lung cancer screening specificity in both U.S.- and Japan-based reader studies, meriting further study in additional international screening environments. Keywords: Assistive Artificial Intelligence, Lung Cancer Screening, CT Supplemental material is available for this article. Published under a CC BY 4.0 license.

Advances in machine learning for health care have brought concerns about bias from the research community; specifically, the introduction, perpetuation, or exacerbation of care disparities. Reinforcing these concerns is the finding that medical images often reveal signals about sensitive attributes in ways that are hard to pinpoint by both algorithms and people. This finding raises a question about how to best design general purpose pretrained embeddings (GPPEs, defined as embeddings meant to support a broad array of use cases) for building downstream models that are free from particular types of bias. The downstream model should be carefully evaluated for bias, and audited and improved as appropriate. However, in our view, well intentioned attempts to prevent the upstream components-GPPEs-from learning sensitive attributes can have unintended consequences on the downstream models. Despite producing a veneer of technical neutrality, the resultant end-to-end system might still be biased or poorly performing. We present reasons, by building on previously published data, to support the reasoning that GPPEs should ideally contain as much information as the original data contain, and highlight the perils of trying to remove sensitive attributes from a GPPE. We also emphasise that downstream prediction models trained for specific tasks and settings, whether developed using GPPEs or not, should be carefully designed and evaluated to avoid bias that makes models vulnerable to issues such as distributional shift. These evaluations should be done by a diverse team, including social scientists, on a diverse cohort representing the full breadth of the patient population for which the final model is intended., Competing Interests: Declaration of interests W-HW, AS, APK, AD’A, JP, RP, SP, VN, SA, AK, HC-L, YM, GSC, DRW, SS, SP, KE, and YL are employees of Google and hold Alphabet stock. W-HW, AS, APK, AD’A, RP, SP, VN, SA, AK, HC-L, YM, GSC, DRW, SS, SP, KE, and YL have patents filed or in progress under Google, broadly related to machine learning and embedding models. CL performs work at Google as a medical consultant via Vituity and receives consulting fees for clinical perspective and guidance. LAGC receives support for educational events and meetings from the National Institutes of Health, Stanford University, University of California San Francisco, University of Toronto, College of Intensive Care Medicine of Australia and New Zealand, University of Bergen, Amsterdam University Medical Centers, Académie Nationale de Médecine (France), and the Doris Duke Foundation (for the Reconsidering Race in Clinical Algorithms workshop at the National Academy of Medicine, Washington, DC). LAGC is an Editor in Chief of PLOS Digital Health, and on the advisory board of the Lancet Digital Health., (Copyright © 2024 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.)

Background The World Health Organization (WHO) recommends chest radiography to facilitate tuberculosis (TB) screening. However, chest radiograph interpretation expertise remains limited in many regions. Purpose To develop a deep learning system (DLS) to detect active pulmonary TB on chest radiographs and compare its performance to that of radiologists. Materials and Methods A DLS was trained and tested using retrospective chest radiographs (acquired between 1996 and 2020) from 10 countries. To improve generalization, large-scale chest radiograph pretraining, attention pooling, and semisupervised learning ("noisy-student") were incorporated. The DLS was evaluated in a four-country test set (China, India, the United States, and Zambia) and in a mining population in South Africa, with positive TB confirmed with microbiological tests or nucleic acid amplification testing (NAAT). The performance of the DLS was compared with that of 14 radiologists. The authors studied the efficacy of the DLS compared with that of nine radiologists using the Obuchowski-Rockette-Hillis procedure. Given WHO targets of 90% sensitivity and 70% specificity, the operating point of the DLS (0.45) was prespecified to favor sensitivity. Results A total of 165 754 images in 22 284 subjects (mean age, 45 years; 21% female) were used for model development and testing. In the four-country test set (1236 subjects, 17% with active TB), the receiver operating characteristic (ROC) curve of the DLS was higher than those for all nine India-based radiologists, with an area under the ROC curve of 0.89 (95% CI: 0.87, 0.91). Compared with these radiologists, at the prespecified operating point, the DLS sensitivity was higher (88% vs 75%, P < .001) and specificity was noninferior (79% vs 84%, P = .004). Trends were similar within other patient subgroups, in the South Africa data set, and across various TB-specific chest radiograph findings. In simulations, the use of the DLS to identify likely TB-positive chest radiographs for NAAT confirmation reduced the cost by 40%-80% per TB-positive patient detected. Conclusion A deep learning method was found to be noninferior to radiologists for the determination of active tuberculosis on digital chest radiographs. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by van Ginneken in this issue.

Background and Aim: The therapeutic procedures used in the treatment of caries do not always eliminate all the microorganisms. Persisting cariogenic bacteria can cause recurrent caries and failure of restoration. Incorporation of an antimicrobial agent in the restorative material may be of paramount significance. The purpose of this study was to evaluate and compare the antibacterial effect of glass ionomer cement (GIC) containing CHX and miswak extract on Streptococcus mutans and Streptococcus sobrinus in ECC children using polymerase chain reaction (PCR)., Materials and Methods: Forty-five children with ECC in the age-group 3-6 years were selected. The children were randomly allocated into three groups. Supragingival plaque samples (S1) were collected from sound buccal or labial surfaces of primary teeth. Cavity preparation was done and the teeth were restored according to the group to which the child had been allotted. The second plaque sample (S2) was collected 1 month and the final sample after 3 months of restoring all the decayed teeth. All the samples were sent for PCR analysis., Results: Intergroup analysis was done using Kruskal-Wallis test followed by Mann-Whitney post hoc test showed statistically significant difference in S. mutans and S. sobrinus count between group I (CHX) and group III (control) and group II (miswak) and group III (control) but no statistically significant difference between group I (CHX) and group II (miswak) in S. mutans and S. sobrinus count., Conclusion: 1% chlorhexidine digluconate and aqueous extract of miswak are equally effective against S. mutans and S. sobrinus . Miswak can be used as an alternative herbal antimicrobial that can be incorporated in anhydrous GIC., How to Cite This Article: Kalpavriksha AJ, Siddaiah SB, Bilichodmath S, et al. Comparative Evaluation of Antibacterial Effect of GIC Containing Chlorhexidine and Miswak on Streptococcus mutans and Streptococcus sobrinus in Early Childhood Caries Children: A PCR Study. Int J Clin Pediatr Dent 2021;14(2):229-234., Competing Interests: Source of support: Department of Pedodontics and Preventive Dentistry, RajaRajeswari Dental College and Hospital, Mysore road, Bengaluru, Karnataka, India Conflict of interest: None, (Copyright © 2021; Jaypee Brothers Medical Publishers (P) Ltd.)

Methicillin-resistant Staphylococcus aureus (MRSA) is a major cause of skin and soft tissue infections. One of the highly successful and rapidly disseminating clones is MRSA ST22 commonly associated with skin tropism. Here we show that a naturally occurring single amino acid substitution (tyrosine to cysteine) at position 223 of AgrC determines starkly different ST22 S. aureus virulence phenotypes, e.g. cytotoxic or colonizing, as evident in both in vitro and in vivo skin infections. Y223C amino acid substitution destabilizes AgrC-AgrA interaction leading to a colonizing phenotype characterized by upregulation of bacterial surface proteins. The colonizing phenotype strains cause less severe skin tissue damage, show decreased susceptibility towards the antimicrobial LL-37 and induce autophagy. In contrast, cytotoxic strains with tyrosine at position 223 of AgrC cause infections characterized by inflammasome activation and severe skin tissue pathology. Taken together, the study demonstrates how a single amino acid substitution in the histidine kinase receptor AgrC of ST22 strains determines virulence properties and infection outcome.

Staphylococcus aureus is a commensal gram positive bacteria which causes severe and non severe infections in humans and livestock. In India, ST772 is a dominant and ST672 is an emerging clone of Staphylococcus aureus. Both cause serious human diseases, and carry type V SCCmec elements. The objective of this study was to characterize SCCmec type V elements of ST772 and ST672 because the usual PCR methods did not amplify all primers specific to the type. Whole genome sequencing analysis of seven ST772 and one ST672 S. aureus isolates revealed that the SCCmec elements of six of the ST772 isolates were the smallest of the extant type V elements and in addition have several other novel features. Only one ST772 isolate and the ST672 isolate carried bigger SCCmec cassettes which were composites carrying multiple ccrC genes. These cassettes had some similarities to type V SCCmec element from M013 isolate (ST59) from Taiwan in certain aspects. SCCmec elements of all Indian isolates had an inversion of the mec complex, similar to the bovine SCCmec type X. This study reveals that six out of seven ST772 S. aureus isolates have a novel type V (5C2) SCCmec element while one each of ST772 and ST672 isolates have a composite SCCmec type V element (5C2&5) formed by the integration of type V SCCmec into a MSSA carrying a SCC element, in addition to the mec gene complex inversions and extensive recombinations.

High genetic variability in viral populations plays an important role in disease progression, pathogenesis, and drug resistance. The last few years has seen significant progress in the development of methods for reconstruction of viral populations using data from next-generation sequencing technologies. These methods identify the differences between individual haplotypes by mapping the short reads to a reference genome. Much less has been published about resolving the population structure when a reference genome is lacking or is not well-defined, which severely limits the application of these new technologies to resolve virus population structure. We describe a computational framework, called Mutant-Bin, for clustering individual haplotypes in a viral population and determining their prevalence based on a set of deep sequencing reads. The main advantages of our method are that: (i) it enables determination of the population structure and haplotype frequencies when a reference genome is lacking; (ii) the method is unsupervised-the number of haplotypes does not have to be specified in advance; and (iii) it identifies the polymorphic sites that co-occur in a subset of haplotypes and the frequency with which they appear in the viral population. The method was evaluated on simulated reads with sequencing errors and 454 pyrosequencing reads from HIV samples. Our method clustered a high percentage of haplotypes with low false-positive rates, even at low genetic diversity.

Staphylococcus aureus is a major human pathogen, first recognized as a leading cause of hospital-acquired infections. Community-associated S. aureus (CA-SA) pose a greater threat due to increase in severity of infection and disease among children and healthy adults. CA-SA strains in India are genetically diverse, among which is the sequence type (ST) 772, which has now spread to Australia, Europe and Japan. Towards understanding the genetic characteristics of ST772, we obtained draft genome sequences of five relevant clinical isolates and studied the properties of their PVL-carrying prophages, whose presence is a defining hallmark of CA-SA. We show that this is a novel prophage, which carries the structural genes of the hlb-carrying prophage and includes the sea enterotoxin. This architecture probably emerged early within the ST772 lineage, at least in India. The sea gene, unique to ST772 PVL, despite having promoter sequence characteristics typical of low expression, appears to be highly expressed during early phase of growth in laboratory conditions. We speculate that this might be a consequence of its novel sequence context. The crippled nature of the hlb-converting prophage in ST772 suggests that widespread mobility of the sea enterotoxin might be a selective force behind its 'transfer' to the PVL prophage. Wild type ST772 strains induced strong proliferative responses as well as high cytotoxic activity against neutrophils, likely mediated by superantigen SEA and the PVL toxin respectively. Both proliferation and cytotoxicity were markedly reduced in a cured ST772 strain indicating the impact of the phage on virulence. The presence of SEA alongside the genes for the immune system-modulating PVL toxin may contribute to the success and virulence of ST772.

We report the draft genome sequence of methicillin-resistant Staphylococcus aureus (MRSA) strain ST672, an emerging disease clone in India, from a septicemia patient. The genome size is about 2.82 Mb with 2,485 open reading frames (ORFs). The staphylococcal cassette chromosome mec (SCCmec) element (type V) and immune evasion cluster appear to be different from those of strain ST772 on preliminary examination.

We report the draft genome sequence of an ST772 Staphylococcus aureus disease isolate carrying staphylococcal cassette chromosome mec (SCCmec) type V from a pyomyositis patient. Our de novo short read assembly is ∼2.8 Mb and encodes a unique Panton-Valentine leukocidin (PVL) phage with structural genes similar to those of ϕ7247PVL and novel lysogenic genes at the N termini.

Background: Diseases from Staphylococcus aureus are a major problem in Indian hospitals and recent studies point to infiltration of community associated methicillin resistant S. aureus (CA-MRSA) into hospitals. Although CA-MRSA are genetically different from nosocomial MRSA, the distinction between the two groups is blurring as CA-MRSA are showing multidrug resistance and are endemic in many hospitals. Our survey of samples collected from Indian hospitals between 2004 and 2006 had shown mainly hospital associated methicillin resistant Staphylococcus aureus (HA-MRSA) carrying staphylococcal cassette chromosome mec (SCCmec) type III and IIIA. But S. aureus isolates collected from 2007 onwards from community and hospital settings in India have shown SCCmec type IV and V cassettes while several variations of type IV SCCmec cassettes from IVa to IVj have been found in other parts of the world. In the present study, we have collected nasal swabs from rural and urban healthy carriers and pus, blood etc from in patients from hospitals to study the distribution of SCCmec elements and sequence types (STs) in the community and hospital environment. We performed molecular characterization of all the isolates to determine their lineage and microarray of select isolates from each sequence type to analyze their toxins, virulence and immune-evasion factors., Results: Molecular analyses of 68 S. aureus isolates from in and around Bengaluru and three other Indian cities have been carried out. The chosen isolates fall into fifteen STs with all major clonal complexes (CC) present along with some minor ones. The dominant MRSA clones are ST22 and ST772 among healthy carriers and patients. We are reporting three novel clones, two methicillin sensitive S. aureus (MSSA) isolates belonging to ST291 (related to ST398 which is live stock associated), and two MRSA clones, ST1208 (CC8), and ST672 as emerging clones in this study for the first time. Sixty nine percent of isolates carry Panton- Valentine Leucocidin genes (PVL) along with many other toxins. There is more diversity of STs among methicillin sensitive S. aureus than resistant ones. Microarray analysis of isolates belonging to different STs gives an insight into major toxins, virulence factors, adhesion and immune evasion factors present among the isolates in various parts of India., Conclusions: S. aureus isolates reported in this study belong to a highly diverse group of STs and CC and we are reporting several new STs which have not been reported earlier along with factors influencing virulence and host pathogen interactions.

A major challenge facing metagenomics is the development of tools for the characterization of functional and taxonomic content of vast amounts of short metagenome reads. The efficacy of clustering methods depends on the number of reads in the dataset, the read length and relative abundances of source genomes in the microbial community. In this paper, we formulate an unsupervised naive Bayes multispecies, multidimensional mixture model for reads from a metagenome. We use the proposed model to cluster metagenomic reads by their species of origin and to characterize the abundance of each species. We model the distribution of word counts along a genome as a Gaussian for shorter, frequent words and as a Poisson for longer words that are rare. We employ either a mixture of Gaussians or mixture of Poissons to model reads within each bin. Further, we handle the high-dimensionality and sparsity associated with the data, by grouping the set of words comprising the reads, resulting in a two-way mixture model. Finally, we demonstrate the accuracy and applicability of this method on simulated and real metagenomes. Our method can accurately cluster reads as short as 100 bps and is robust to varying abundances, divergences and read lengths.