Huriez syndrome: a rare palmoplantar keratoderma

The Huriez syndrome is a rare autosomal dominant transgradient palmoplantar keratoderma which is characterized by scleroatrophy of the fingers, nail changes and squamous cell carcinomas in affected skin. Herein, we present a non-familial case of very rare plamoplantar keratoderma with scleroatrophy - the Huriez syndrome in a 45 year old female patient

Development of E Waste based Composite Microwave Absorbing Material

Microwave absorbing materials (MAMs) are widely researched due to their use in many practical applications including both civil and defense sectors. Irrespective of the humongous efforts of various researchers, the development of a wide bandwidth, thin coating thickness, and low-cost microwave absorber is still a challenging task. The existing materials have not been able to meet all the specifications together at once and require a trade-off in the performance parameters. In this paper, we have empirically corroborated a cost-effective technique using E-waste material for synthesising composite MAM. It is herein shown that the addition of different wt% of copper, graphite, and titanium dioxide in the E-waste successfully resulted in enhanced absorption due to altered electrical properties of the E-waste suitable for microwave absorption. The multilayering technique with the help of a genetic algorithm has also been used to broaden the bandwidth. As a result, a three-layer MAM with the total coating thickness of 3.2 mm has been synthesised showing the wideband absorption bandwidth of 8.47 GHz in the frequency range from 6.92 to 15.39 GHz. The results suggested that microwave absorption of E-waste can be drastically improved by appropriately tailoring electrical parameters such as permittivity and permeability

Sporotrichosis in Sub-Himalayan India

Sporotrichosis is endemic in the Sub-Himalayan belt, which ranges from the northern to the north-eastern Indian subcontinent. Similar to many parts of the developing world, sporotrichosis is commonly recognized clinically in this region however consolidated epidemiological data is lacking. We report epidemiological, clinical and microbiological data from a hundred culture positive cases of sporotrichosis. Out of 305 clinically suspicious cases of sporotrichosis, a total of 100 isolates were identified as Sporothrix schenckii species complex (S. schenckii) on culture. Out of the culture proven cases 71% of the cases presented with lymphocutaneous type of lesions while 28% had fixed localized type and 1% had disseminated sporotrichosis. Presentation with lesions on hands was most frequently seen in 32% with arm (23%) and face (21%) in that sequence. The male to female ratio was 1∶1.27. Age ranged from 1 ½ years to 88 years. Mean age was 43.25 years. Disease was predominantly seen in the fourth to sixth decade of life with 58% cases between 31 and 60 years of age. Since the first report from the region there has been a steady rise in the number of cases of sporotrichosis. Seasonal trends reveal that most of the patients visited for consultation in the beginning of the year between March and April. This is the first study, from the most endemic region of the Sub-Himalayan belt, to delve into epidemiological and clinical details of such a large number of culture proven cases over a period of more than eighteen years which would help in the understanding of the local disease pattern of sporotrichosis

Empowering recommender systems using automatically generated Knowledge Graphs and Reinforcement Learning

Personalized recommendations have a growing importance in direct marketing, which motivates research to enhance customer experiences by knowledge graph (KG) applications. For example, in financial services, companies may benefit from providing relevant financial articles to their customers to cultivate relationships, foster client engagement and promote informed financial decisions. While several approaches center on KG-based recommender systems for improved content, in this study we focus on interpretable KG-based recommender systems for decision making.To this end, we present two knowledge graph-based approaches for personalized article recommendations for a set of customers of a large multinational financial services company. The first approach employs Reinforcement Learning and the second approach uses the XGBoost algorithm for recommending articles to the customers. Both approaches make use of a KG generated from both structured (tabular data) and unstructured data (a large body of text data).Using the Reinforcement Learning-based recommender system we could leverage the graph traversal path leading to the recommendation as a way to generate interpretations (Path Directed Reasoning (PDR)). In the XGBoost-based approach, one can also provide explainable results using post-hoc methods such as SHAP (SHapley Additive exPlanations) and ELI5 (Explain Like I am Five).Importantly, our approach offers explainable results, promoting better decision-making. This study underscores the potential of combining advanced machine learning techniques with KG-driven insights to bolster experience in customer relationship management.Comment: Accepted at KDD (OARS) 2023 [https://oars-workshop.github.io/

Clinico-epidemiological profile of sexually transmitted infections in patients attending a tertiary health care hospital in southern Himachal Pradesh: A retrospective study

Introduction: Sexually transmitted infections (STIs) are a public health problem and are a burden to the individual, his family and community. The prevalence of STIs is very high in developing nations including India and varies widely across different regions. There is enormous need to study the pattern of STIs in various regions of country for implementation of control strategies. Aims: To estimate the prevalence and to study the clinico-epidemiological profile and trends of sexually transmitted infections in patients attending the STI clinic of a tertiary care hospital in southern Himachal Pradesh. Material and methods: Records of patients attending the STI clinic during last two years i e from January 2019 to December 2020 was retrieved and analysed retrospectively. Results: Vulvovaginal candidiasis was the most common (non viral) STI seen in 624(39.3%) patients. While genital warts (8.7%), molluscum contagiosum (8.2%) and herpes genitalis (7.2%) were the common viral STIs. Bacterial STIs like gonococcal urithritis(7.9%), chancroid (6.6%), bacterial vaginosis (6.3%), non gonococcal urithritis (3.9%), lymphogranuloma venereum (LGV) (3.6%) and non gonococcal cervicitis (3%) were not uncommon. Rapid plasma regain test (RPR) was found to be reactive in 24(1.5%) patients, out of which, 14(0.9%) were males and 10(0.6%) were females. HIV seropositivity was seen in 2(0.2%) patients and both of them were males. Conclusion: Fungal STI was more common as compared to viral STIs. Trend for viral STIs is increasing and that for bacterial STIs is declining among STI clinic attendees, which is consistent with other studies from different regions

Deep convolution neural network model to predict relapse in breast cancer

A mishap in anti-cancer drug distribution is critical in breast cancer patients due to poor prediction model to identify the treatment regime in ER+ve and ER-ve (Estrogen Receptor (ER)) patients. The traditional method for the prediction depends on the change in expression across the normal-disease pair. However, it certainly misses the multidimensional aspect and underlying cause of relapse, such as various mutations, drug dosage side effects, methylation, etc. In this paper, we have developed a multi-layer neural network model to classify multidimensional genomics data into their similar annotation group. Further, we used this multi-layer cancer genomics perceptron for annotating differentially expressed genes (DEGs) to predict relapse based on ER status in breast cancer. This approach provides multivariate identification of genes, not just by differential expression, but, cause-effect of disease status due to drug overdosage and genomics-driven drug balancing method. The multi-layered neural network model, where each layer defines the relationship of similar databases with multidimensional knowledge. We illustrate that the use of multilayer knowledge graph with gene expression data for training the deep convolution neural network stratify the patient relapse and drug dosage along with underlying molecular properties.This publication has emanated from research conducted with the financial support of Science Foundation Ireland (SFI) under Grant Number SFI/12/RC/2289, co-funded by the European Regional Development Fundnon-peer-reviewe

Comparison of CPAP preoxygenation versus conventional preoxygenation on duration of safe apnoea time in patients undergoing elective surgery under general anaesthesia

Anesthesia induction usually leads to apnoea, during apnoea, oxygenation depends on the oxygen reserves stored within the body .While breathing room air these stores are quantitatively low. As we cannot perfectly predict the difficulty in airway management, desirability of maximal preoxygenation is theoretically present for all patients. Induction of general anaesthesia per se as also the use of 100% oxygen during preoxygenation results in the development of atelectasis in dependent lung regions within minutes of anaesthetic induction. Therefore this randomized, controlled study was undertaken to compare the effect of CPAP (continuous positive airway pressure) preoxygenationvs conventional preoxygenation on duration of safe apnoea time in patients posted for elective surgery under general anaesthesia. After obtaining approval from the institutional review board and institutional ethics committee and prior consent from participants, 60 adult patients scheduled for elective surgery under general anaesthesia were randomized into two groups- PEEP group and ZEEP group. Patients in PEEP group were preoxygenated with CPAP of 5 cm of H2O with 100% oxygen for five minutes and in ZEEP group no CPAP was used. Duration of safe apnoea time (taken as till Spo2 reached 94%) and ABG analysis at various time intervals was done for each group.The comparison of normally distributed continuous variables between the groups was performed using Student’s t test. Nominal categorical data between the groups were compared using Chi-squared test or Fisher’s exact test as appropriate. P value less than 0.05 was considered statistically significant. We found out that the duration of safe apnoea time was significantly longer in PEEP group (408.90 ± 32.73) as compared to ZEEP group (257.70 ± 12.79 s) ( P value less than 0.001).PaO2 after preoxygenation was also significantly higher in PEEP group (416.62 ± 28.72 mmHg) as compared to ZEEP group (367.02 ± 14.29 mmHg) ( P value less than 0.001).We concluded that the application of continuous positive airway pressure during preoxygenation is a simple, well tolerated technique that may have advantages especially in those patients in whom difficulty in airway management is anticipated, those who are at increased risk of desaturation such as morbidly obese patients and when assisted ventilation is not applied such as during rapid sequence induction